WHITE PAPER Automated Workload Transformation to : The Future-proof Approach Introduction

Enterprises have been dependent on traditional data warehouses to ingest, model, and store data for ages. In a typical IT environment, conventional data warehouses would deploy, extract, transform, and load (ETL) jobs to process data in batches. However, handling millions of queries per month come at a considerable cost. Blending petabytes and exabytes of data from various historical and streaming sources such as internal data across spreadsheets, third-party data, and stores also make business analysis difficult and time consuming.

As businesses explore options to shift from traditional data warehouses to meet their demands and scale business operations, the open stack-based cloud platform has gained popularity. With multiple cloud players such as AWS, Azure, IBM Cloud, and Cloud Platform offering services, choosing the right cloud provider can be complicated.

According to Gartner, by 2020, enterprises will spend around $411 billion in public cloud1.

To realize the full potential of the cloud, enterprises are partnering with cloud service providers. For example, has partnered with AWS; IBM is partnering with Workday and Google Cloud.

RightScale surveyed 997 respondents for the 2018 State of the Cloud Report to measure cloud adoption.

1https://www.forbes.com/sites/louiscolumbus/2017/10/18/cloud-computing-market-projected-to-reach-411 b-by-2020/#1d68e01a78f2

2https://www.zdnet.com/article/cloud-providers-ranking-2018-how-aws-microsoft-google-cloud-platform-ib mcloud-oracle-alibaba-stack/ Enterprise Public Cloud Adoption 2018 vs. 2017 % of Respondents Running Applications

AWS 68% 59% Azure 58% 43% Google Cloud 19% 15% IBM Cloud 15% 10% VMware Cloud on AWS 12% Not asked in 2017 10% 2018 5% 2% 2017 Not asked in 2017

Source: RightScale 2018 State of the Cloud Report

Whether it is public, private, or hybrid, enterprises are continuing to move their workloads and applications to the cloud infrastructure. According to IDC3, 90 percent of organizations will invest in the hybrid cloud market by 2020. Gartner predicts that more than 50 percent organizations using the cloud today will have all their workloads in the cloud by 2021.

Worldwide Cloud IT Infrastructure Market Forecast by Development Type, 2015 - 2021 (shares based on value)

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2015 2016 2017 2018 2019 2020 2021 Traditional DC Private Cloud Public Cloud

Source: IDC 2018

3https://www.idc.com/getdoc.jsp?containerId=prUS43508918 3 What does it mean? Enterprises will no longer lift-and-shift into the cloud but will instead refactor and rebuild directly in the cloud. While enterprises might retain some mission-critical workloads on-premise, most enterprise data will be in the cloud. Key considerations include: • Reasons to move to the cloud • Challenges of cloud adoption • Five steps to move to the cloud • Managing the transformation

Reasons to move to the cloud

The following are the three top reasons that enterprises are moving to the cloud: Reducing operational overheads Gartner4 reveals that companies spend more than 70 percent of their IT budgets on hardware, maintenance, and support of internal systems. Cloud can help enterprises reduce costs. For example, a subscription-based model allows enterprises to lower costs by reducing rack space, power usage, and maintenance costs.

Boosting governance With more enterprises moving to the cloud, providers are working round-the-clock to ensure higher levels of data governance. This means enterprises no longer need to invest a lot of time and money configuring security features.

Increasing flexibility and control With the increased data workloads, enterprises often struggle to ramp up their infrastructure. Moving to the cloud addresses scalability and allows managers to focus on innovation without worrying about flexibility. With a single network connecting several cloud environments to on-premise data centers, enterprises can manage both regular and critical workloads with ease.

4http://www.gartner.com/downloads/public/explore/metricsAndTools/ITBudget_Sample_2012.pdf 4 Challenges of cloud adoption

Moving data workloads to the cloud presents its own set of challenges. Examples include ensuring data protection, transferring access controls, maintaining licensing and compliance, minimizing transfer costs, and preventing downtime and availability issues.

When considering cloud adoption, enterprises need to align on their business goals and objectives and budget accordingly. This includes addressing dependencies and application compatibility. If implementing a hybrid data warehousing model, enterprises need to identify the workloads they need to shift, and integrate and manage data across a wide range of data systems, ranging from mainframe to the cloud. According to Forbes:

63% Say that Digitally transforming their enterprise is the leading IT factor for public cloud adoption today. Professionals

66% Say Security is the biggest concern in adopting an enterprise IT strategy. Professionals

50% Believe Artificial Intelligence and Machine Learning are playing a IT role in cloud computing adoption today, growing to 67% by 2020. Professionals

Five steps to move to the cloud

Until recently, many enterprises retained mission-critical workloads on traditional data warehouses. However, with increased data governance, zero downtime, and the low cost of maintenance on the cloud, enterprises are considering moving even their mission-critical workloads. By automating the entire process, enterprises can eliminate error-prone, slow, costly manual practices, and mitigate their risks.

5 When moving your workloads to the cloud, ensure the following: Identify your rationale and analyze the risks Why move to the cloud? Identify potential benefits and risks. Does it make sense for your business?

Assess your workloads and environment Identify which workloads you need to move to the cloud to ensure effective maintenance, utilization of resource, and ROI. With an automated transformation tool, you might consider moving your legacy data warehouse to the cloud. Determine the cost savings.

Select the right cloud partner If you do not have the technical know-how to move to the cloud, look for a cloud partner who can help you move your data and other critical workloads like views, queries, reports, and ETL workloads. Some key aspects to consider while choosing a partner are: • Automation coverage • Support for data store • Past experiences • Clients and their testimonials • Certifications • Customer service Choose the cloud computing model that you would want to deploy depending on the nature and need of your business. Some of the common service models are as follows: • Infrastructure-as-a-Service (IaaS): Examples include , Azure, AWS, and Alibaba Cloud • Platform-as-a-Service (PaaS): Examples include , AWS Elastic Beanstalk, , and • -as-a-Service (SaaS): Examples include Google Apps, NetSuite, Office 365, and Salesforce • Data warehouse-as-a-Service (DWaaS): Examples include SQL Data Warehouse, IBM dashDB, Snowflake Computing, and Redshift

6 Plan the cloud adoption After you have identified and assessed the workloads to move and determined which cloud service provider best meets your needs, consider the following: • How to minimize downtime • Identify and track key metrics to define the success of the migration • Timelines • Changes to end-user processes post-adoption • Training needs post-adoption Move your workloads After you have planned the adoption and have identified the workloads to move, follow these steps to execute the plan smoothly:

Backup your existing data and servers in a safe environment, and make sure it is easily retrievable. Backup

Set up the cloud environment. Consider using an automated tool to set up provisioning, connections, and testing all the components individually and Deploy in totality.

Once deployed, existing data must be moved to the cloud to ensure business continuity. Also, some changes might be required to the current Move data to fit the new setup.

Once shifted to the cloud, test the connections to ensure everything is working as expected in a secured environment. Make sure that end users Test will not face any issues while accessing the data.

Monitor the workloads to identify and fix problems that might crop up in the cloud environment. Monitor

7 How Impetus can help

Data transformation success depends on the data that is being moved as well as a proven approach. Here’s how to make it work:

Data-driven EDW workload assessment Does your data warehouse have the capacity to align your business goals with the required SLAs? Prioritize the right workloads to move to the cloud and implement an optimal future big data environment. Create data-driven assessments and employ insight-driven offloading recommendations to mitigate risks and save time and effort.

Automated EDW transformation Transform to a big data warehouse with extended capabilities while protecting and reusing all your EDW investments. Do not simply move data but transform your scripts, reports, and ETL workflows to a big data compatible equivalent. Transform your logic to the most optimum query engine and generate a variety of output artifacts in Java, SQL, Scala, Python, etc. that exceeds your SLAs. Predict your query performance to realize the most out of your new environment.

Automated validation framework Transform your existing inventory of workloads and ensure that they are safe. The automated validation framework enables you to confirm and verify your data, metadata, and logic in the cloud. Maintain the same level of compliance at all stages of cloud adoption and conform to your SLAs to ensure complete automation of the entire cloud adoption lifecycle. Apply standard or custom validation rules to assure the correctness and meaningfulness of the shifted workloads.

Automated execution of transformed workloads Schedule the execution of transformed workloads on any standard big data environment over the cloud. To learn more, visit www.impetus.com or write to us at [email protected].

Impetus is focused on creating big business impact through Big Data Solutions for Fortune 1000 enterprises across multiple verticals. The company brings together a unique mix of software products, consulting services, Data Science capabilities, and technology expertise. It offers full life-cycle services for Big Data implementations and real-time streaming analytics, including technology strategy, solution architecture, proof of concept, production implementation and on-going support to its clients.

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