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The rapid advancement of cloud computing has brought in new possibilities for public institutions and private enterprises. With near-infinite resources and scalability, ease of setup, provisioning with IAAS, PAAS, and SAAS models, and pay-as-you-go features, the cloud has opened up opportunities and frontiers that were simply not possible in the yesteryears.

Scientific research is one such discipline. Sectors that depend on research for their relevance and impact have been able to make rapid advances using cloud computing. Scientific research requires vast compute, storage resources for ingesting and analyzing very large data sets, and specialized tools and services for data analysis and visualization. Healthcare and life sciences are one such sector where research happens on a mega-scale across public and private institutions funded by various governments and private sector entities.

Typical Characteristics of Research Ecosystem
There are some defining characteristics in a research ecosystem, which lends themselves naturally to cloud-based computing environments.

  • Research happens with a community of researchers working towards common objectives and goals
  • Budgets get defined based on proposals and typically need to be allocated to research teams and tracked closely for consumption
  • Ability to set up and operate secure and trusted environments for sensitive data and compute engines with specialized pipelines, analysis, and visualization tools
  • Ability to share research outcomes with sponsors and fellow researchers in a simple manner
  • Requirement to put massive amounts of storage, compute and specialized and advanced computing, analysis and visualization at fingertips and make it insanely simple to access for scientific researchers who are not necessarily IT savvy to be able to do create and operate complex IT infrastructure

Cloud Portals for Scientific Research
The above characteristics and requirements have opened up a new class of solutions called Cloud Self Service Portals, which typically provide a curated set of tools and datasets for researchers to access the ability to track their budget consumption, simple one-click provisioning, and managing the life cycle of cloud resources necessary for scientific research and ability to share research outcomes with fellow researchers.

RLCatalyst Research Gateway and Use Cases
Relevance Lab, with its Cloud Self Service Portal, RLCatalyst Research Gateway, has been leading the way in providing highly simplified access to a curated set of cloud services for the Scientific Research community. The following are some of the leading use cases satisfied by the use of RLCatalyst Research Gateway.


Sl No. Use Case Description
1. Genomic Pipeline Processing and using High Performance Computing in AWS Cloud (AWS Batch, Parallel Cluster, Illumina Dragen/NVidia Parabriks Pipeline processing) Genomic pipeline processing using Nextflow and Nextflow Tower (open source) solution integrated with AWS Batch for easy deployment of open source pipelines and associated cost tracking per researcher and per pipeline
2. Secure and Scalable RStudio on AWS Need an RStudio solution on AWS Cloud with an ability to connect securely (using SSL) without having to worry about managing custom certificates and their lifecycle
3. Provide researchers ability to access their core EC2 based tools and easy access to large and secure data sets in cloud for “trusted research” Enable researchers with EC2 Linux and Windows servers to install their specific research tools and software. Ability to add AMI based researcher tools (both private and from AWS Marketplace) with 1-click on MyResearchCloud
4. AI/ML Analytics Workbench Using SageMaker AI/ML Workbench drive Data research (like COVID-19 impact analysis) with available public data sets already on AWS cloud and create study-specific data sets
5. Enabling small groups with proper Cost and Budget control with Self-service models (bring your own License) Enable a small group of Principal Investigator and researchers to manage Research Grant programs with tight budget control, self-service provisioning, and research data sharing

Summary
Cloud Service Portals are a class of solutions, which hide the complexity of setting up and operating cloud environments for resource-intensive activities such as scientific research and provide researchers with more time to focus on the science. Relevance Lab, with its solution RLCatalyst Research Gateway, has been a key enabler, having solved several use cases (mentioned above in this blog) related to scientific research for genomics, life sciences, and pharma sectors.

To know more about how RLCatalyst Research Gateway can meet your scientific research requirements, please contact us at marketing@relevancelab.com.

Reference Links
Driving Frictionless Scientific Research with Self-Service Cloud Portal
How to speed up the GEOS-Chem Earth Science Research using AWS Cloud?
Leveraging AWS HPC for Accelerating Scientific Research on Cloud
Health Informatics and Genomics on AWS with RLCatalyst Research Gateway
Enabling Researchers with Next-Generation Sequencing (NGS) Leveraging Nextflow and AWS
8-Steps to Set-Up RLCatalyst Research Gateway



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2021 Blog, Blog, Feature Blog, Featured

Relevance Lab’s focus on addressing the digital transformation jigsaw puzzle has a strategic investment in leveraging Products & Platforms to create a unique differentiation and competitive advantage. We are a specialist cloud, DevOps, automation, and analytics services company with an IP (Intellectual Property) led technology strategy. This helps our customers achieve frictionless business outcomes by leveraging cloud across their infrastructure, applications, and data.

We optimize IT spending with smart cloud workload migration, reducing ongoing operations costs by leveraging automation & governance, speeding up innovation in the delivery of new software products with Agile & DevOps, and getting key real-time business insights with Actionable Analytics.

The key platforms and playbooks that we have are the following:


RLCatalyst provides an “Automation-First” approach for Enterprise Cloud Orchestration across “Plan, Build & Run” lifecycle, leveraging our unique IP. A pre-built library of quick-starts, BOTs and Open source solutions helps customers use “Cloud The Right Way” focused on best practices like “Infrastructure as Code” and “Compliance as Code”. We also have unique specialization on AWS and ServiceNow platforms leveraged to provide Intelligent Cloud Operations & managed services with our ServiceOne platform covering workload migration, security, governance, CMDB, ITSM, and DevOps.

SPECTRA provides a Digital and Agile Analytics platform that helps build enterprise data lakes and Supply Chain analytics with multiple ERP systems connectors (SAP, Oracle, Dynamics, JDE, etc.). It also provides a smart-document search engine for Google-like features on enterprise digital documents (images, PDF, engg drawings, etc.). We leverage the Digital platforms for Frictionless Application modernization and Cloud Product Engineering services extending across platforms covering content, commerce, CRM, and supply chain (Adobe, Shopify, SFDC, Oracle Fusion, Azure PowerApps, Services & ADF) integrated with actionable insights from SPECTRA.


The figure above explains our company’s focus in driving frictionless IT and business operations leveraging these key platforms. The focus on a “coded business model” that the platforms deliver help us engage across the full lifecycle with customers covering the following stages:

  • Assess the key customer needs as each customer has a unique model we evaluate based on 3C’s (Culture, Content & Constraints)
  • Standardize the internal systems, processes, engineering practices, and governance
  • Automate everything repetitive impacting speed, costs, quality, and compliance
  • Accelerate the achievement of business objectives with faster software delivery, better operational excellence, and real-time Agile Analytics

RLCatalyst Platform and ServiceOne Solution
RLCatalyst is an intelligent automation platform built with DevOps, Cloud, and Automation features covering infrastructure, applications, data, and workflows. RLCatalyst common services foundation is built using an open architecture in line with the industry standards and can be customized. On top of the foundation services, a set of specialized products, solutions, and services are created to cover the specialized needs of customers. Following are a few key foundation themes for RLCatalyst:

  • Built on Open-source products to provide flexibility and scalability for hybrid cloud deployments
  • Uses “Infrastructure as Code” best practices and DevOps standards covering CI/CD, end to end monitoring, and orchestration
  • The platform is built to have a UI Portal front-end, Node.JS API-based backend, integration layer for executing BOTs, and database layer based on NoSQL
  • The core concept uses a “self-aware” paradigm to embed dynamic configurations, end to end monitoring, and dynamic CMDB to enable smart operations using other ITSM and Cloud platforms
  • The Cloud Portal drives self-service models of DevOps and can be customized to add domain-specific business rules for customers or industry type
  • There is “Compliance as Code” embedded into the design to make sure customers can be aligned with well-architected principles
  • The platform is built on top of AWS and ServiceNow ecosystem but can also be deployed on-prem or other cloud platforms
  • The solutions are pre-integrated with other popular DevOps and Cloud tools like Docker, Chef, Anisible, Terraform, Jenkins, ELK, Sensu, Consul, etc
  • The platform comes with a pre-built library of BOTs and Quickstart templates

The combination of RLCatalyst and ServiceOne integrated solution provides an intelligent automation architecture, as explained in the figure below. The key building blocks are:

  • Discover the underlying assets, health, costs, vulnerability, security, and compliance.
  • Automate using a framework of BOTs built with self-aware intelligence covering tasks, workflows, decisioning, and AI/ML algorithms.
  • Resolve at speed all service management tickets and requests with complex workflows & integration across multiple systems

SPECTRA Platform and Business Process Automation
SPECTRA, the AI-driven Analytics platform from Relevance Lab, based on Open Source technology, can fast track your journey from data to actionable insights. It can process data from structured data from different ERP Systems based on pre-existing adapters and unstructured data from PDFs, Emails, engineering drawings, and commercial labels. Every organization has invested in a combination of tools, technologies and solutions to create their Data platforms. However, most of these platforms are built with legacy technologies or fragmented components. When companies try to leverage the new technologies of Big Data, Cloud Architectures and Artificial Intelligence to achieve more meaningful Analytics a pre-built Platform like SPECTRA can save tremendous efforts, costs and time to provide a scalable and flexible alternative.

Similar to the RLCatalyst IT Optimization we leverage SPECTRA Platform for Business Optimization with Agile Analytics, as explained in figure below.


We have also leveraged SPECTRA Platform and UiPath Integration to Achieve business process hyper automation, as explained briefly below.


Customer Impact with RL Playbooks for IT and Business Transformation
Relevance Lab leverages our strengths in platforms for all our customer engagements to bring out special value on services delivery in areas of:

  • Cloud Infrastructure Automation
  • Data Analytics Platforms
  • Digital Applications and Product Engineering
  • Intelligent Operations and DevOps

The figure below highlights the value created for some of our key customers.


We have adopted the following maturity model as a specialist technology company with significant investments on competency and IP creation that guides the investments in RLCatalyst and SPECTRA platforms.


Level-1 Deep Technology Expertise Continuous learning and skills upgrade on latest/emerging Technologies focus across Cloud, Automation, Analytics, DevOps, Digital
Level-2 Focus on Certifications – Basic & Advanced Promoting “Industry Certifications” to benchmark the competencies against the global standards and make this part of every developer’s career enhancement goal
Level-3 Solutions and Best Practices (Process & Tools) Focus on customer solutions and recurring use cases to build a knowledge base of best practices across software engineering, operations excellence, business domains
Level-4 Platform Focus “Codified Knowledge” in the form of Platforms for Data Analytics, DevOps, Cloud & Automation with source code in re-usable formats. Well-Architected Frameworks and leveraging open-source platforms with custom component enhancements & integrations to save effort, time, and improved quality with each new engagement
Level-5 Product Offerings Prescriptive and pre-created products that customers can use in a “touchless” manner as SaaS or Marketplace offerings like a typical ISV solution with little or no dependency on associated professional services. Major benefit in enabling frictionless jumpstart on specific business problems.

Summary
Relevance Lab has made significant investments in creating IT and Business Transformation platforms that provide us a key competitive advantage in unlocking value for our customers across DevOps, Analytics, Cloud, Automation and Digital Engineering. By following a service maturity model that goes beyond just headcount and competencies we have been able to bring the value of platform and products to solve the digital transformation puzzle for our 50+ customers.

To know more about how can our products and platforms help feel free to contact us marketing@relevancelab.com.



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2021 Blog, Blog, Featured

The CMS-1500 form is vital to the smooth functioning of the American health insurance system, and yet processing these manually filled-up, paper-based forms can be a nightmare. Relevance Lab has developed a new approach to automating claims processing that improves output quality and delivers cost savings.

CMS-1500 Processing Challenges
The CMS-1500 form, formerly known as the HCFA-1500 form, is the standard claim form used by a non-institutional provider or supplier to bill Medicare carriers under certain circumstances. Processing these important documents presents several challenges:


  • Large volume of information: A single form has 33 fields (plus sub-fields) to be filled up manually; multi-page claims are required if more than 6 services are provided.
  • Illegible handwriting: Since the forms are filled manually (and often in a hurry), it is quite common to find illegible or difficult to read entries.
  • Incomplete or inconsistent information: Fields are often missing or inconsistent (e.g. multiple spellings of the same name), complicating the task.
  • Poor scan quality: The scan quality can be poor due to misorientation of the form, folds, etc., making it difficult to recognize text.

Most users are faced with a Hobson’s choice between costly manual processing and low-accuracy conventional automation solutions, neither of which produce acceptable results. Manual processing can be slow, laborious, fatigue-prone and costs tend to grow linearly with claims volumes, regardless of whether the manpower is in-house or outsourced. Conventional automation solutions based on simple scanning and optical character recognition (OCR) techniques struggle to deal with such non-standardized data leading to high error rates.

Relevance Lab has developed a solution to address these issues and make CMS-1500 claims processing simpler and more cost-efficient without compromising on accuracy.

Relevance Lab’s Smart Automation Solution
Our solution enhances the effectiveness of automation by utilizing artificial intelligence and machine learning techniques. At the same time, the workflow design ensures that the final sign-off on form validation is provided by a human.
An illustrative solution architecture is given below:


Salient features of the solution are as follows:


Best-in-class OCR The solution utilizes the Tesseract open-source OCR engine (supported by Google), which delivers a high degree of character recognition accuracy, to convert the scanned document into a searchable pdf.
Processing & Validation against master data The document is analyzed using RL’s proprietary SPECTRA platform. Common errors (such as misaligned check-box entries) are corrected, and relevant fields are validated against the master data to catch anomalies (e.g. spelling errors).
Assisted human review The updated document is presented for human review. Fields that require attention are highlighted, together with algorithm-generated guesstimates suggesting possible corrections.
Automatic update of downstream data Once approved, downstream systems are automatically updated with validated data.
Self-learning The iterative self-learning algorithm improves with every validation cycle resulting in continuous refinement in accuracy. This improvement can be tracked over time through built-in trackers.
Workflow tracking The solution is equipped with dashboards that enable tracking the progress of a document through the cycle.
Role-based access It is possible to enable role-based access to different modules of the system to ensure data governance.


The following diagram presents a typical process flow incorporating our solution:


Demonstrated Benefits
Our CMS-1500 processing solution delivers significant time and cost savings, and quality improvements. It frees up teams from tedious tasks like data entry, without compromising human supervision and control over the process. The solution is scalable, keeping costs low even when processing volumes increase manifold.

The solution is tried, tested, and proven to deliver substantial value. For example, in a recent implementation at a renowned healthcare provider, the Relevance Lab SPECTRA solution was able to reduce the claims processing time by over 90%. Instead of a dedicated team working daily to process claim forms, manual intervention is now required only once a week for review and approvals. The resources freed up are now more productively utilized. This has also led to an increase in accuracy through the elimination of “human errors” such as typos.

Powered by the RL SPECTRA analytics platform, the solution has successfully delivered productivity gains to multiple clients by efficiently ingesting and processing structured and unstructured data. The plug-and-play platform is easy to integrate with most common system environments and applications.

Conclusion
CMS-1500 claims processing can be significantly optimized by using Relevance Lab’s intelligent solution based on its SPECTRA platform that combines the speed and scalability of automation with the judgment of a human reviewer to deliver substantial productivity gains and cost savings to organizations.

For more details, please feel free to reach out to marketing@relevancelab.com.



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2021 Blog, Blog, Featured, ServiceOne

Relevance Lab helps customers use cloud “The Right Way” with an Automation-First approach as part of our Governance360 solution offering. Customers implementing this solution go through a maturity model covering the following stages:

  • Basic Governance using AWS best practices with Control Tower, Security Hub, Management & Governance Lens
  • Advanced Governance with automation-led approach and deep integration with service management tools, vulnerability assessment, and remediations
  • Proactive and Preventive Governance with integrated end-to-end monitoring
  • Intelligent compliance with remediations

As part of achieving this maturity model, it is important to have proper IT asset management, vulnerability assessment, and remediation models. A critical aspect of addressing infrastructure-level vulnerabilities depends on a smart patch management process. Patch management is a key part of your IT Operations to avoid potential exploitation and to ensure vulnerabilities are addressed on time by patching your systems, which includes operating systems, applications, and network components across your on-premises, cloud, or a hybrid setup.

As shown below, patch management is a pivotal layer of security management and starts with the identification of assets from your asset inventory, followed by vulnerability assessment, patch management, security information & event management (SIEM), and visualization in the form of dashboards/reports.


Let us see the steps to automate the entire lifecycle of patch management as shown in the below picture along with some industry-standard tools/platforms.


  • Step 1: All vulnerabilities pertaining to operating systems and software are captured through periodic scans using agents and analyzed.
  • Step 2: Using patching solutions, identify the missing patches and co-relate this to the vulnerabilities being addressed.
  • Step 3: Based on the criticality of the servers like Dev, Test, Prod, or criticality of the patches, the assets are identified for patching. A Change Request (CR) is raised with the details of what to patch, along with the patching windows, and the asset owners.
  • Step 4: Create a backup/snapshot before the patching activity and check for the patching client/agent availability on the servers planned for patching.
  • Step 5: Patch the servers during the agreed window, and if successful, CR is updated accordingly. In case of failure, CR is updated with a failure status.
  • Step 6: Post the patching activity, re-run the vulnerability scan to ensure all patch-related vulnerabilities are addressed and taken care of. The servers are also validated for the functionality of the applications before the CR can be closed.

Use Case Benefits for Customers
By automating patch management, customers can have near real-time visibility to the security compliance of their infrastructure and ensure an ongoing periodic process of patching is enabled, and having a 360-view of their IT infrastructure using dashboards. Enabling automated patching can save a lot of time and resources.

Compliance Benefits:

  • Secured and centralized way of monitoring dashboard
  • Automated patching
  • Optical consistency across all businesses
  • Providing ease of security auditing
  • Periodic & timely notifications of the compliance/non-compliance status report to IT teams or individuals

The IT team can create their own custom patch baselines and decide which patches to auto-approve by using the following categories.


  • Operating Systems: Windows, Amazon Linux, Ubuntu Server, etc.
  • Product Name: e.g. RHEL 6.5, Amazon Linux 2014.089, Windows Servers 2012, Windows Server 2012 R2, etc.
  • Classification: Critical updates, security updates, etc.
  • Severity: Critical, important, etc.

Use Case of Hybrid Setup Patch Management
As shown in the sample below, there are 2 environments Prod, and Dev, referred to as Patch Groups. This helps to avoid deploying patches to the wrong set of instances. A patch group must be defined with the tag key Patch Group. For example, we have created a patch group tag key called Dev below. A fleet of instances that have these tags can be patched using this approach.


Details of the Architecture

  • AWS Systems Manager gathers asset inventory details and a pre-configured maintenance window automatically scans for the latest patches for the server groups at a scheduled time.
  • The automated patch function lambda is scheduled to run daily to collect the patch group and maintenance window details. It also creates the patch group and maintenance schedule tags on the managed instances.
  • This lambda function then creates or updates the right patch groups and maintenance schedules, associates the patch groups with the patch baselines, configures the patch scans, and deploys the patching task. You can also notify users of impending patches using CloudWatch Events.
  • As per the maintenance schedule, the events will send patch notifications to the application teams with the details of the impending patch operation.
  • Patch Manager then initiates the patching based on the predefined window and patch groups.
  • Details about patching are retrieved using resources data sync in Systems Manager and published to a S3 bucket.
  • Using this data from the S3 bucket, you can build a visualization dashboard about the patch compliance in Amazon QuickSight.

As explained earlier, visualization is an essential layer showing the near real-time security status of your IT infrastructure. These can be a dashboard, as shown below.


Getting Started
Patch Management is available as a professional service offering and also as an AWS marketplace offering under Governance360. Below are the steps to take the customer from discovery to steady state.


Step-1 Discovery Assess the current landscape of Process & Tools/Technology
Step-2 Recommend Present the current gaps and benchmark against industry standards
Step-3 Plan and Implement Design and implement the proposed solution in a phased manner
Step-4 Ongoing Bring the solution to a stable state/BAU (Business As Usual)

Conclusion
In this blog post, we covered the key aspects of automated patch management for enterprises. Relevance Lab has implemented automated patch management solutions, which is part of our Automation Factory Suite for its key customers bringing in better detection, assessment and compliance for their Cloud Governance. The entire solution is available as a re-usable framework that can save new enterprises 6+ months of time, efforts and costs for new deployments.

To know more about our Governance360 offering and its building blocks, including automated patch management, feel free to contact marketing@relevancelab.com.

References
Automated Patch Management for Cloud & Data Centers



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2021 Blog, Blog, Feature Blog, Featured

While helping our customers with the right way to use the cloud using an Automation-First approach, the primary focus from Relevance Lab is to enable significant automation (achieved 70%+ for large customers) of day-to-day tasks with benefits on the speed of delivery, quality improvements, and cost reduction. Large customers have complex organizational structures with different groups focussing on infrastructure automation, application deployment automation, and service delivery automation. In many cases, there is a missing common architecture in planning, building, and running a proper end-to-end automation program. To help enterprises adopt an Automation-First approach for cloud adoption covering all three aspects of infrastructure, applications, and service delivery, we help create a blueprint for an Automation Factory.

In this blog, we are sharing our approach for large customers with a complex landscape of infrastructure and applications. The focus of this blog is more on application deployment automation with custom and COTS (commercial off-the-shelf) products in Cloud.

Some of the most typical asks by customers with all their workloads in AWS Cloud is captured below:


  • Separation of roles between common infrastructure teams and multiple business units managing their own application needs
  • Infrastructure teams provide base AMI with CloudFormation stacks to provide basic OS-level compute workloads to application groups, who manage their own deployments
  • Application groups deal with a set of custom Java + .NET applications and COTS products, including Oracle Fusion Middleware stacks
  • Application groups manage the complete lifecycle of deployment and support in production environments
  • Application deployments are about 20% containerized and 80% direct installations in hybrid scenarios with legacy codebases
  • Different set of tools are used along with homegrown custom scripts
  • Primary pain points are to automate application and product (COTS) build and deploy lifecycle across different environments and upgrades
  • The solution is expected to leverage DevOps maturity and automation-led standardization for speed and flexibility
  • Need guidance on the choice of Automation Factory model between mutable vs. immutable designs

Key requirements from application groups are shared below based on the snapshot of products for which there is a need for automated installation and scalability at run-time. The shift needs to happen from “handcrafting” product installations to automated and easy deployment, preferably with immutable infrastructure.


Standard Products COTS Products (High Priority) COTS Products (Good to have)
Weblogic Oracle E-Business Suite (Financial Portal) Cisco UCM
Tomcat 7, 8, & 9 OBIEE Kofax
Apache Oracle Discoverer IBM Business Rules Engine
IIS 10 Oracle Siebel CRM Aspect
Oracle 19 Microsoft SQL Server Reporting Service Avaya
SQL Server Oracle Fusion AS/400 Apps
MS SQL SAP Enterprise Adobe AEM


Relevance Lab Approach for Hyperautomation with RLCatalyst and BOTs
Our teams have implemented 50+ engagements across customers and created a mature automation framework to help re-use and speed up the need for an Automation Factory using RLCatalyst BOTs and RLCatalyst Cloud Portals.

The figure below explains the RLCatalyst solutions for hyperautomation leveraging the Automation Service Bus (ASB) framework that allows easy integration with existing customer tools and cloud environments.


The key building block of automation depends on the concept of BOTs. So what are BOTs?


  • BOTs are automation codes managed by Automation Service Bus orchestration
    • Infrastructure creation, updation, deletion
    • Application deployment lifecycle
    • Operational services, tasks, and workflows – Check, Act, Sensors
    • Interacting with Cloud and On-prem systems with integration adapters in a secure and auditable manner
    • Targeting any repetitive Operations tasks managed by humans – frequently, complex (time-consuming), security/compliance related
  • What are types of BOTs?
    • Templates – CloudFormation, Terraform, Azure Resource Models, Service Catalog
    • Lambda functions, Scripts (PowerShell/python/shell scripts)
    • Chef/Puppet/Ansible configuration tools – Playbooks, Cookbooks, etc.
    • API Functions (local and remote invocation capability)
    • Workflows and state management
    • UIBOTs (with UiPath, etc.) and un-assisted non-UI BOTs
    • Custom orchestration layer with integration to Self-Service Portals and API Invocation
    • Governance BOTs with guardrails – preventive and corrective
  • What do BOTs have?
    • Infra as a code stored in source code configuration (GitHub, etc.)
    • Separation of Logic and Data
    • Managed Lifecycle (BOTs Manager and BOTs Executors) for lifecycle support and error handling
    • Intelligent Orchestration – Task, workflow, decisioning, AI/ML

Proposed Solution to Customers
There are different approaches to achieving end-to-end automation, and the right solution depends on a proper assessment of the context of customer needs. Relevance Lab follows a consultative approach that helps do a proper assessment of customer needs, priorities, and business goals to create the right foundation and suggest a maturity model for an Automation Factory. Also, different engagement models are offered to customers covering the entire phase of the Plan-Build-Run lifecycle of automation initiatives, including organization design and change management.

The following table helps plan the right approach and maturity model to be adopted for BOTs targeting different levels of complexity for automation.


BOT Complexity Functionality Coverage Leveraging Relevance Lab Products and Solutions
Level-1 Standard Cloud Resources Provisioning in a secure, multi-account covering compute, storage and data EC2 Linux, EC2 Win, S3 Buckets, RDS, SageMaker, ALB, EMR, VPC, etc. with AWS Service Catalog AWS Console and ITSM Portals RLCatalyst Cloud Portal, BOTs Server CI/CD Pipelines with BOTs APIs
Level-2 Standard Applications deployment covering Middleware, Databases, Open Source Applications requiring single node setup. Single Node COTS setups can also be included though more complex Tomcat, Apache, MySQL, NGINX – common Middleware and Database Stacks Portal, CI/CD Pipeline, CLI Variants:
– Option-1 AMI Based (Preferred model for Immutable design)
– Option- 2 Docker Based
– Option- 3 Chef/Ansible Post Provision App Install & Configure (Mutable Design)
BUILD Phase – Covering Plan, Build, Test, Publish Lifecycle
CONSUME Phase – Production Deploy & Upgrade Lifecycle
Level-3 Multi-tier Applications – 2-Tier, 3-Tier, N-Tier with Web + App + DB, etc. combinations Required a combination of Infra, Apps, Post provision configurations, and orchestration. Complex Infra with ALB, PaaS Service Integrations Orchestration engine and service discovery/registry Docker and Kubernetes clusters
Level-4 Complex Business Apps – ERP, Oracle EBS, COTS, HPC Clusters – not supporting standard Catalog Items. Complex workflows with integration to multiple Third-Party systems UI or System Driven Custom Orchestration Flows and workflow modules Event-driven and state management Post provisioning complex integrations Pre-built BOTs Library

Leveraging a combination of Relevance Lab products and solutions, we provide a mature Automation Factory blueprint to our customers, as shown below.


The above solution is built leveraging best practices from AWS Well-Architected framework and bringing in a combination of AWS tools and other third-party solutions like HashiCorp, Ansible, Docker, Kubernetes, etc. The key building blocks of the Automation Factory cover the following tools and concepts:


  • AWS AMI Builder Factory and Golden AMI concept
  • HashiCorp Packer Scripts
  • OS and Hardening with Ansible
  • Vulnerability Assessment and Patch Management
  • AWS Inspector, AWS Parameter Store, AMI Catalog publishing, Multi-Account AWS Best Practices
  • AWS Service Catalog, Multi-Account Governance, Master and Consumption accounts
  • Self-Service Cloud Portals with guard-rails and automated fulfilment
  • CI/CD Pipelines for non-user assisted workflows using RLCatalyst BOTs, Terraform Templates, Jenkins, Docker, and Kubernetes
  • Monitoring and diagnostics with Observability tools like RLCatalyst Command Center
  • Ongoing Governance, Cost Management, Lifecycle Management, Blue-Green Deployments, and Container Management
  • Cloud Accounts, VPC Automation, AWS Control Tower, AWS Management, and Governance Lens Automation

Summary
The journey to adopting an Automation-First approach requires a strong foundation that our Automation Factory solution offers, saving at least 6 months of in-house efforts and about US$250K worth of savings for large customers annually. The BOTs deployed can scale up to provide productivity gains of 4-5 people full-time employees with other benefits of better fulfillment SLAs, quality, and compliance gains. In the case of COTS deployments, especially with Oracle stacks, our BOTs have reduced the time of deployments from a few weeks to a few hours.

To know more about how can we help your automation journey, feel free to contact marketing@relevancelab.com.

Reference Links
Considerations for AWS AMI Factory Design
AWS Well-Architected Best Practices
ASB – A New Approach for Intelligent and Touchless Automation



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2021 Blog, Research Gateway, Blog, Featured

We aim to enable the next-generation cloud-based platform for collaborative research on AWS with access to research tools, data sets, processing pipelines, and analytics workbench in a frictionless manner. It takes less than 30 minutes to launch a “MyResearchCloud” working environment for Principal Investigators and Researchers with security, scalability, and cost governance. Using the Software as a Service (SaaS) model is a preferable option for Scientific research in the cloud with tight control on data security, privacy, and regulatory compliances.

Typical top-5 use cases we have found for MyResearchCloud as a suitable solution for unlocking your Scientific Research needs:

  • Need an RStudio solution on AWS Cloud with an ability to connect securely (using SSL) without having to worry about managing custom certificates and their lifecycle
  • Genomic pipeline processing using Nextflow and Nextflow Tower (open source) solution integrated with AWS Batch for easy deployment of open source pipelines and associated cost tracking per researcher and per pipeline
  • Enable researchers with EC2 Linux and Windows servers to install their specific research tools and software. Ability to add AMI based researcher tools (both private and from AWS Marketplace) with 1-click on MyResearchCloud
  • Using SageMaker AI/ML Workbench drive Data research (like COVID-19 impact analysis) with available public data sets already on AWS cloud and create study-specific data sets
  • Enable a small group of Principal Investigator and researchers to manage Research Grant programs with tight budget control, self-service provisioning, and research data sharing

MyResearchCloud is a solution powered by RLCatalyst Research Gateway product and provides the basic environment with access to data, workspaces, analytics workbench, and cloud pipelines, as explained in the figure below.


Currently, it is not easy for research institutes, their IT staff, and a group of principal investigators & researchers to leverage the cloud easily for their scientific research. While there are constraints with on-premise data centers and these institutions have access to cloud accounts, converting a basic account to one with a secured network, secured access, ability to create & publish product/tools catalog, ingress & egress of data, sharing of analysis, tight budget control and other non-trivial tasks divert the attention away from ‘Science’ to ‘Servers’.

We aim to provide a standard catalog for researchers out-of-the-box solution with an ability to also bring your own catalog, as explained in the figure below.


Based on our discussions with research stakeholders, especially small & medium ones, it was clear that the users want something as easy to consume as other consumer-oriented activities like e-shopping, consumer banking, etc. This led to the simplified process of creating a “MyResearchCloud” with the following basic needs:


  • This “MyResearchCloud” is more suitable for smaller research institutions with a single or a few groups of Principal Investigators (PI) driving research with few fellow researchers.
  • The model to set up, configure, collaborate, and consume needs to be extremely simple and comes with pre-built templates, tools, and utilities.
  • PI’s should have full control of their cloud accounts and cost spends with dynamic visibility and smart alerts.
  • At any point, if the PI decides to stop using the solution, there should be no loss to productivity and preservation of existing compute & data.
  • It should be easy to invite other users to collaborate while still controlling their access and security.
  • Users should not be loaded with technical jargon while ordering simple products for day-to-day research using computation servers, data repositories, analysis IDE tools, and Data processing pipelines.

Based on the above ask, the following simple steps have been enabled:


Steps to Launch Activity Total time from Start
Step-1 As a Principal Investigator, create your own “MyResearchCloud” by using your Email ID or Google ID to login the first time on Research Gateway. 1 min
Step-2 If using a personal email ID, get an activation link and login for the first time with a secure password. 4 min
Step-3 Use your own AWS account and provide secure credentials for “MyResearchCloud” consumption. 10 min
Step-4 Create a new Research Project and set up your secure environment with default networking, secure connections, and a standard catalog. You can also leverage your existing setup and catalog. 13 min
Step-5 Invite new researchers or start using the new setup to order your products to get started with a catalog covering data, compute, analytic tools, and workflow pipeline. 15 min
Step-6 Order the necessary products – EC2, S3, Sagemaker/RStudio, Nextflow pipelines. Use the Research Gateway to interact with these tools without the need to access AWS Cloud console for PI and Researchers. 30 min


The picture below shows the easy way to get started with the new Launchpad and 30 minutes countdown.


Architecture Details
To balance the needs of Speed with Compliance, we have designed a unique model to allow Researchers to “Bring your own License” while leveraging the benefits of SaaS in a unique hybrid approach. Our solution provides a “Gateway” model of hub-and-spoke design where we provide and operate the “Hub” while enabling researchers to connect their own AWS Research accounts as a “Spoke”.

Security is a critical part of the SaaS architecture with a hub-and-spoke model where the Research Gateway is hosted in our AWS account using best practices of Cloud Management & Governance controlled by AWS Control Tower while each tenant is created using AWS security best practices of minimum privileges access and role-based access so that no customer-specific keys or data are maintained in the Research Gateway. The architecture and SaaS product are validated as per AWS ISV Path program for Well-Architected principles and data security best practices.

The following diagram explains in more detail the hub-and-spoke design for the Research Gateway.


This de-coupled design makes it easy to use a Shared Gateway while connecting your own AWS Account for consumption with full control and transparency in billing & tracking. For many small and mid-sized research teams, this is the best balance between using a third-party provider-hosted account and having their own end-to-end setup. This structure is also useful for deploying a hosted solution covering multiple group entities (or conglomerates), typically covering a collaborative network of universities working under a central entity (usually funded by government grants) in large-scale genomics grants programs. For customers who have more specific security and regulatory needs, we do allow both the hub-and-spoke deployment accounts to be self-hosted. The flexible architecture can be suitable for different deployment models.


AWS Services that MyResearchCloud uses for each customer:


Service Needed for Secure Research Solution Provided Run Time Costs for Customers
Need for DNS-based friendly URL to access MyResearchCloud SaaS RLCatalyst Research Gateway No additional costs
Secure SSL-based connection to my resources AWS ACM Certificates used and AWS ALB created for each Project Tenant AWS ALB implemented smartly to create and delete based on dependent resources to avoid fixed costs
Network Design Default VPC created for new accounts to save users trouble of network setups No additional costs
Security Role-based access provided to RLCatalyst Research Gateway with no keys stored locally No additional costs. Users can revoke access to RLCatalyst Research Gateway anytime.
IAM Roles AWS Cognito based model for Hub No additional costs for customers other than SaaS user-based license
AWS Resources Consumption Directly consumed based on user actions. Smart features are available by default with 15 min auto-stop for idle resources to optimize spends. Actual usage costs that is also suggested for optimization based on Spot instances for large workloads
Research Data Storage Default S3 created for Projects with the ability to have shared Project Data and also create private Study Data. Ability to auto-mount storage for compute instances with easy access, backup, and sync with base AWS costs
AWS Budgets and Cost Tracking Each project is configured to track budget vs. actual costs with auto-tagging for researchers. Notification and control to pause or stop consumption when budgets are reached. No additional costs.
Audit Trail All user actions are tracked in a secure audit trail and are visible to users. No additional costs
Create and use a Standard Catalog of Research Products Standard Catalog provided and uploaded to new projects. Users can also bring their own catalogs No additional costs.
Data Ingress and Egress for Large Data Sets Using standard cloud storage and data transfer features, users can sync data to Study buckets. Small set of files can also be uploaded from the UI. Standard cloud data transfer costs apply

In our experience, research institutions can enable new groups to use MyResearchCloud with small monthly budgets (starting with US $100 a month) and scale their cloud resources with cost control and optimized spendings.

Summary
With an intent to make Scientific Research in the cloud very easy to access and consume like typical Business to Consumer (B2C) customer experiences, the new “MyResearchCloud” model from Relevance Lab enables this ease of use with the above solution providing flexibility, cost management, and secure collaborations to truly unlock the potential of the cloud. This provides a fully functional workbench for researchers to get started in 30 minutes from a “No-Cloud” to a “Full-Cloud” launch.

If this seems exciting and you would like to know more or try this out, do write to us at marketing@relevancelab.com.

Reference Links
Driving Frictionless Research on AWS Cloud with Self-Service Portal
Leveraging AWS HPC for Accelerating Scientific Research on Cloud
RLCatalyst Research Gateway Built on AWS
Health Informatics and Genomics on AWS with RLCatalyst Research Gateway
How to speed up the GEOS-Chem Earth Science Research using AWS Cloud?
RLCatalyst Research Gateway Demo
AWS training pathway for researchers and research IT



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2021 Blog, AppInsights Blog, Blog, Featured, ServiceOne

Relevance Lab announces the availability of a new product RLCatalyst AppInsights on ServiceNow Store. The certified standalone application will be available free of cost and offers a dynamic application-centric view of AWS resources.

Built on top of AWS Service Catalog AppRegistry and created in consultations with AWS Teams, the product offers a unique solution for ServiceNow and AWS customers. It offers dynamic insights related to cost, health, cloud asset usage, compliance, and security with the ability to take appropriate actions for operational excellence. This helps customers to manage their multi-account dynamic application CMDB (Configuration Management Database).

The product includes ServiceNow Dashboards with metrics and actionable insights. The design has pre-built connectors to AWS services and unique RL DataBridge that provides integration to third-party applications using serverless architecture for extended functionality.

Why do you need a Dynamic Application-Centric View for Cloud CMDB?
Cloud-based dynamic assets create great flexibility but add complexity for near real-time asset and CMDB tracking, especially for enterprises operating in a complex multi-account, multi-region, and multi-application environment. Such enterprises with complex cloud infrastructures and ITSM tools, struggle to change the paradigm from infrastructure-centric views to application-centric insights that are better aligned with business metrics, financial tracking and end user experiences.

While existing solutions using Discovery tools and Service Management connectors provided a partial solution to an infrastructure-centric view, a robust Application Centric Dynamic CMDB was a missing solution that is now addressed with this product. More details about the features of this product can be found on this blog.

Built on AWS Service Catalog AppRegistry
AWS Service Catalog AppRegistry helps to create a repository of your applications and associated resources. These capabilities enable enterprise stakeholders to obtain the information they require for informed strategic and tactical decisions about cloud resources.

Leveraging AWS Service Catalog AppRegistry as the foundation for the application-centric views, RLCatalyst AppInsights enhances the value proposition and provides integration with ServiceNow.

Value adds provided:

  • Single pane of control for Cloud Operational Management with ServiceNow
  • Cost planning, tracking, and optimization across multi-region and complex cloud setups
  • Near real-time view of the assets, health, security, and compliance
  • Detection of idle capacity and orphaned resources
  • Automated remediation

This enables the entire lifecycle of cloud adoption (Plan, Build and Run) to be managed with significant business benefits of speed, compliance, quality, and cost optimization.

Looking Ahead
With the new product now available on the ServiceNow store, it makes easier for enterprises to download and try this for enhanced functionality on existing AWS and ServiceNow platforms. We expect to work closely with AWS partnership teams to drive the adoption of AWS AppRegistry Service Catalog and solutions for TCAM (Total Cost of Application Management) in the market. This will help customers optimize their application assets tracking and cloud spends by better planning, monitoring, analyzing and corrective actions, through an intuitive UI-driven ServiceNow application at no additional costs.

To learn more about RLCatalyst AppInsight, feel free to write to marketing@relevancelab.com.



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2021 Blog, SPECTRA Blog, Blog, Featured

Oracle Fusion is an invaluable support to many businesses for managing their transaction data. However, business users would be familiar with limitations when it comes to generating even moderately complex analyses and reports involving a large volume of data. In a Big Data-driven world, this can become a major competitive disadvantage. Relevance Lab has designed SPECTRA, a Hadoop-based platform, that makes Oracle Fusion reporting reports simple, quick, and economical even when working with billions of transaction records.

Challenges with Oracle Fusion Reporting
Oracle Fusion users often struggle to extract reports from large transactional databases. Key issues include:


  • Inability to handle large volumes of data to generate accurate reports within reasonable timeframes.
  • Extracting integrated data from different modules of the ERP is not easy. It requires manual effort for synthesizing fragmented reports, which makes the process time-consuming, costly, and error-prone. Similar problems arise when trying to combine data from the ERP with that from other sources.
  • The reports are static, not permitting a drill down on the underlying drivers of reported information.
  • There are limited self-service operations, and business users have to rely heavily on the IT department for building new reports. It is not uncommon for weeks and months to pass between the first report request and the availability of the report.

Moreover, Oracle has stopped supporting its reporting tool Discoverer from 2017, creating additional challenges for users that continue to rely on it.

How RL SPECTRA can Help
Relevance Lab recognizes the value to its clients of generating near real-time dynamic insights from large, ever-growing data volumes at reasonable costs. With that in mind, we have developed an Enterprise Data Lake (EDL) platform, SPECTRA, that automates the process of ingesting and processing huge volumes of data from the Oracle Cloud.

This Hadoop-based solution has advantages over traditional data warehouses and ETL solutions due to its:


  • superior performance through parallel processing capability and robustness when dealing with large volumes of data,
  • rich set of components like Spark, AI/ML libraries to derive insights from big data,
  • a high degree of scalability,
  • cost-effectiveness, and
  • ability to handle semi-structured and unstructured data.

After the initial data ingestion into the EDL, incremental data ingestion uses delta refresh logic to minimize the time and computing resources spent on ingestion.

SPECTRA provides users access to raw data (based on authorization) empowering them to understand and analyze data as per their requirement. It enables users to filter, sort, search & download up to 6 million records at one go. The platform is also capable of visualizing data in charts, apart from being compatible with standard dashboard tools.

This offering combines our deep Oracle and Hadoop expertise with extensive experience across industries.
With this solution, we have helped companies generate critical business reports from massive volumes of underlying data delivering substantial improvement in extraction and processing time, quality, and cost-effectiveness.


Use Case: Productivity Enhancement through Optimised Reporting for a Publishing Major
A global publishing major that had recently deployed Oracle Fusion Cloud applications for inventory, supply chain, and financial management discovered that these were inadequate to meet its complex reporting and analytical requirements.


  • The application was unable to accurately process the company’s billion-plus transaction records on the Oracle Fusion Cloud to generate a report on the inventory position.
  • It was also challenging to use an external tool to do this as it would take several days to extract data from the Oracle cloud to an external source while facing multiple failures during the process.
  • This would make the cost and quality reconciliation of copies of books lying in different warehouses and distribution centres across the world very difficult and time-consuming, as business users did not have on-time and accurate visibility of the on-hand quantity.
  • In turn, this had adverse business consequences such as inaccurate planning, higher inventory costs, and inefficient fulfilment.

The company reached out to Relevance Lab for a solution. Our SPECTRA platform automated and optimized the process of data ingestion, harmonization, transformation, and processing, keeping in mind the specific circumstances of the client. The deployment yielded multiple benefits:


  • On-Hand quantity and costing reports are now generated in less than an hour
  • Users can access raw data as well as multiple reports with near real-time data, giving them full flexibility and making the business more responsive to market dynamics
  • Overall, user efforts stand reduced by 150 hours per person per quarter by using SPECTRA for their inventory report, leading to higher productivity
  • With all the raw data in SPECTRA, several reconciliation procedures are in place to identify missing data between the Oracle cloud and its legacy system

The Hadoop-based architecture can be scaled flexibly in response to the continuously growing size of the transaction database and is also compatible with the client’s future technology roadmap.


Conclusion
RL’s big-data platform, SPECTRA, offers an effective and efficient future-ready solution to the reporting challenges in Oracle Fusion when dealing with large data sets. SPECTRA enables clients to access near real-time insights from their big data stored on the Oracle Cloud while delivering substantial cost and time savings.

To know more about our solutions or to book a call with us, please write to marketing@relevancelab.com.



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2021 Blog, Research Gateway, Blog, Featured

Bioinformatics is a field of computational science that involves the analysis of sequences of biological molecules (DNA, RNA, or protein). It’s aimed at comparing genes and other sequences within an organism or between organisms, looking at evolutionary relationships between organisms, and using the patterns that exist across DNA and protein sequences to elucidate their function. Being an interdisciplinary branch of the life sciences, bioinformatics integrates computer science and mathematical methods to reveal the biological significance behind the continuously increasing biological data. It does this by developing methodology and analysis tools to explore the large volumes of biological data, helping to query, extract, store, organize, systematize, annotate, visualize, mine, and interpret complex data.

The advances in Cloud computing and availability of open source genomic pipeline tools have provided researchers powerful tools to speed up processing of next-generation sequencing. In this blog, we explain leveraging the RLCatalyst Research Gateway portal to help researchers focus on science and not servers while dealing with NGS and popular pipelines like RNA-Seq.

Steps and Challenges of RNA-Seq Analysis
Any Bioinformatics analysis involving next-generation Sequencing, RNA-Seq (named as an abbreviation of “RNA Sequencing”) constitutes of these following steps:


  • Mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events
  • Quantifying expression levels of genes, transcripts, and exons
  • Differential analysis of gene expression among different biological conditions
  • Biological interpretation of differentially expressed genes

As seen from the figure below, the RNA-Seq analysis for identification of differentially expressed genes can be carried out in one of the three (A, B, C) protocols, involving different sets of bioinformatics tools. In study A, one might opt for TopHat, STAR, and HISAT for alignment of sequences and HTSeq for quantification, whereas the same set of steps can be performed by using Kalisto and Salmon tools (Study B) or in combination with CuffLinks (Study C) all of these yields the same results which are further used in the identification of differentially expressed genes or transcripts.


Each of these individual steps is executed using a specific bioinformatics tool or set of tools such as STAR, RSEM, HISAT2, or Salmon for gene isoform counting and extensive quality control of the sequenced data. The major bottlenecks in RNA-Seq data analysis include manual installations of software, deployment platforms, or computational capacity and cost.

Looking at the vast number of tools available for a single analysis and different versions and their compatibility makes the setup tricky. This can also be time-consuming as proper configuration and version compatibility assessment take several months to complete.

Nextflow: Solution to Bottleneck
The most efficient way to tackle these hurdles is by making use of Nextflow based pipelines that support cloud computing where virtual systems can be provisioned at a fraction of the cost, and the setup is seemingly smoother that can be done by a single individual, as well as support for container systems (Docker and Singularity).

Nextflow is a reactive workflow framework and a programming DSL (Domain Specific Language) that eases the writing of data-intensive computational pipelines.

As seen in the diagram below, the infrastructure to use Nextflow in the AWS cloud consists of a head node (EC2 instance with Nextflow and Nextflow Tower open source software installed) and wired to an AWS Batch backend to handle the tasks created by Nextflow. AWS Batch creates worker nodes at run-time, which can be either on-demand instances or spot instances (for cost-efficiency). Data is stored in an S3 bucket to which the worker nodes in AWS Batch connect and pull the input data. Interim data and results are also stored in S3 buckets, as is the output. The pipeline to be run (e.g. RNA-Seq, DualRNA-Seq, ViralRecon, etc.) is pulled by the worker nodes as a container image from a public repo like DockerHub or BioContainers.

RLCatalyst Research Gateway takes care of provisioning the infrastructure (EC2 node, AWS Batch compute environment, and Job Queues) in the AWS cloud with all the necessary controls for networking, access, data security, and cost and budget monitoring. Nextflow takes care of creating the job definitions and submitting the tasks to Batch at run-time.

The researcher initiates the creation of the workspace from within the RLCatalyst Research Gateway portal. There is a wide selection of parameters as input including, which pipeline to run, tuning parameters to control the sizing and cost-efficiency of the worker nodes, the location of input and output data, etc. Once the infrastructure is provisioned and ready, the researcher can connect to the head node via SSH and launch Nextflow jobs. The researcher can also connect to the Nextflow Tower UI interface to monitor the progress of jobs.


The pre-written Nextflow pipelines can be pulled from an nf-core GitHub repository and can be set up within minutes allowing the entire analysis to run using a single line command, and the results of each step are displayed on the command line/shell. Configuration of the resources on the cloud is seamless as well, since Nextflow based pipelines provide support for batch computing, enabling the analysis to scale as it progresses. Thus, the researchers can focus on running the pipeline and analysis of output data instead of investing time in setup and configurations.

As seen from the pipeline output (MultiQC) report of the Nextflow-based RNA-Seq pipeline below, we can identify the sequence quality by looking at FastQC scores, identify duplication scenarios based on the contour plots as well as pinpoint the genotypic biotypes along with fragment length distribution for each sample.


RLCatalyst Research Gateway enables the setting up and provisioning of AWS cloud resources with few simple clicks for such analysis, and the output of each run is saved in a S3 bucket enabling easy data sharing. These provisioned resources are pre-configured setups with a proper design template and security architecture and added to these features. RLCatalyst Research Gateway enables cost tracking for the currently running projects, which can be paused/ stopped or deleted as per convenience.

Steps for Running Nextflow-Based Pipelines in AWS Cloud for Genomic Research
Prerequisites for a researcher before starting data analysis.

  • A valid AWS account and access to the RLCatalyst Research Gateway portal
  • A publicly accessible S3 bucket with large Research Data sets accessible

Once done, below are the steps to execute this use case.

  • Login to the RLCatalyst Research Gateway Portal and select the project linked to your AWS account
  • Launch the Nextflow-Advanced product
  • Login to the head node using SSH (Nextflow software will already be installed on this node)
  • In the pipeline folder, modify the nextflow.config file to set the data location according to your needs (Github repo, S3 bucket, etc.). This can also be passed via the command line
  • Run the Nextflow job on the head node. This should automatically cause Nextflow to submit jobs to the AWS Batch backend
  • Output data will be copied to the Output bucket specified
  • Once done, terminate the EC2 instance and check for the cost spent on the use case
  • All costs related to the Nextflow project and researcher consumption are tracked automatically

Key Points

  • Bioinformatics involves developing methodology and analysis tools to analyze large volumes of biological data
  • Vast number of tools available for a single analysis and their compatibility make the analysis setup tricky
  • RLCatalyst Research Gateway enables the setting up and provisioning of Nextflow based pipelines and AWS cloud resources with few simple clicks

Summary
Researchers need powerful tools for collaboration and access to commonly used NGS pipelines with large data sets. Cloud computing makes it much easier with access to workflows, data, computation, and storage. However, there is a learning curve for researchers to use Cloud-specific knowhow and how to use resources optimally for large-scale computations like RNA-Seq analysis pipelines that can also be quite costly. Relevance Lab working closely with AWS partnership has provided RLCatalyst Research Gateway portal to use commonly used pre-built Nextflow pipeline templates and integration with open source repositories like nf-core and biocontainers. RLCatalyst Research Gateway enables execution of such Nextflow-based scalable pipelines on the cloud with few clicks and configurations with cost tracking and resource execution control features. By using AWS Batch the solution is very scalable and optimized for on-demand consumption.

For more information, please feel free to write to marketing@relevancelab.com.

References



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2021 Blog, AWS Governance, Blog, Featured

As enterprises continue to rapidly adopt AWS cloud, the complexity and scale of operations on the AWS have increased exponentially. Enterprises now operate hundreds and even thousands of AWS accounts to meet their enterprise IT needs. With this in mind, AWS Management & Governance has emerged as a major focus area that enterprises need to address in a holistic manner to ensure efficient, automated, performant, available, secure, and compliant cloud operations.


Governance360 integrated with Dash ComplyOps

Governance360
Relevance Lab has recently launched its Governance360 professional services offering in the AWS Marketplace. This offering builds upon Relevance Lab’s theme of helping customers adopt AWS the right way.

Governance360 brings together the framework, tooling, and process for implementing a best-practices-based AWS Management & Governance at scale for multi-account AWS environments. It helps clients seamlessly manage their “Day after Cloud” operations on an ongoing basis. The tooling that would be leveraged for implementing Governance360 can include AWS’s native tools, services, RL’s tools, and third-party industry tools.

Typically a Governance360 type of professional service is engaged either during or after the phase of customers’ transition to AWS cloud (Infra & application migration or development on AWS Cloud).

Dash ComplyOps Platform
Dash ComplyOps platform enables and automates the lifecycle of a client’s journey for compliance of their AWS environments towards industry-specific compliance requirements such as HIPAA, HITRUST, SOC2, GDPR. Dash ComplyOps platform provides organizations with the ability to manage a robust cloud security program through the implementation of guardrails and controls, continuous compliance monitoring, and advanced reporting and remediation of security and compliance issues.


Relevance Lab and Dash Solutions have partnered together to bring an end-to-end solution and professional service offering that helps customers realize an automated AWS Management & Governance posture for their environments meeting regulatory compliance needs.
As a part of this partnership, the Dash ComplyOps platform is integrated within the overall Governance360 framework. The detailed mapping of features, tooling, and benefits (including Dash ComplyOps as a tool) across Governance360’s major topic areas is articulated in the table below.

Benefits


Governance360 Topic Benefits
Automation Lifecycle
  • Automate repetitive and time-consuming tasks
  • Automated setup of environments for common use cases such as regulatory, workloads, etc
  • Codify best practices learned over time
  • Control Services
  • Automated & Standardized Account Provisioning
  • Cost & Budget Management
  • Architecture for Industry Standard Compliance, Monitoring, and Remediation
  • Disaster Recovery
  • Automated & Continuous Compliance Monitoring, Detection, and Remediation
  • Proactive Monitoring
  • Dashboards for monitoring AWS Environment from infrastructure to application
  • Security Management
  • Ease of Deployment of Security Controls @ Scale using CI/CD Pipeline
  • Infra and Application Security Threat Monitoring, Prevention, Detection & Remediation
  • Service & Asset Management
  • Software and Asset Management practice with real-time CMDB for Applications & Infrastructure
  • Incident management and auto-remediation
  • Workload Migration & Management
  • Best practices-based workload migration and implementations on AWS cloud
  • Regulatory Compliance
  • Compliance with industry regulatory standards


  • Engagement Flow

    Engagement Flow / Phase Details Typical Duration*
    Discovery & Assessment
  • Understand current state, data, management & governance goals
  • 1-3 weeks
    Analysis & Recommendation
  • Requirement analysis, apply Governance360 framework, create recommendations & obtain client sign off
  • Recommendations include services, tools, and dashboards & expected outcomes, benefits
  • Use of native AWS services, RL’s monitoring & BOTs, Dash ComplyOps platform, and other 3rd party tools
  • 1-3 weeks
    Implementation
  • Implement, test, UAT & production cutover of recommended services, tools, and dashboards
  • 2-8 weeks
    Hypercare
  • Post-implementation support – monitor and resolve any issues faced
  • 1-2 weeks

    * Duration depends on the complexity and scope of the requirements.

    Summary
    Relevance Lab is a consulting partner of AWS and helps organizations achieve automation-led Cloud Management using Governance360, based on the best practices of AWS. For enterprises with regulatory compliance needs, integration with the Dash ComplyOps platform provides an advanced setup for operation in a multi-account environment. While enterprises can try to build some of these solutions, it is both time-consuming and error-prone and demands a specialist partner. Relevance Lab has helped multiple enterprises with this need and has a reusable automated solution and pre-built library to meet the security and compliance needs of any organization.

    For more details, please feel free to contact marketing@relevancelab.com.

    References
    Governance 360 – Are you using your AWS Cloud “The Right Way”
    AWS Security Governance for enterprises “The Right Way”
    Dash ComplyOps by Dash Solutions
    Governance360 available on AWS Marketplace



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