Several intermediate streams can be introduced depending on the current state of the customer’s cloud journey path, but here are some phases that help set the right foundation for building a robust architecture: TEKsystems proposes executing such large-scale technical projects in a phased manner. TEKsystems developed a multi-step approach toward retiring a data warehouses and migrating it to Amazon Redshift. Working through a series of well-defined brainstorming exercises as well as RACI (responsible, accountable, consulted, informed) discussions, a modern analytical framework (see Figure 1) was envisioned. As a global provider of technology, business, and talent solutions, TEKsystems works in lockstep with customers in their business transformation. Align Vision and Roadmapīefore initiating any series of activities, it’s critical to align with the cultural ethos and business aspirations of the customer. The goal is not to do a pure lift and shift, but to implement a methodical modernization of components that makes sense for your business. Transfer and optimize processes defined within the database.The TEKsystems toolkit’s key objectives are to provide efficient ways to: However, the toolkit can help assist move data warehouses across several other platforms (Oracle and SQL Server, for example) into Amazon Redshift or Amazon Relational Database Service (Amazon RDS). Here, we will discuss a real customer use case of moving data assets from an on-premises data warehouse into Amazon Redshift, a modern cloud data warehouse. TEKsystems is also a member of the AWS Well-Architected Partner Program and holds the Amazon Redshift Ready designation. TEKsystems Global Services is an AWS Advanced Tier Services Partner with Competencies in DevOps as well as Data and Analytics. This has resulted in several innovations which combined to form a “TEKsystems Cloud Migration Toolkit.” With several years of experience in migration and modernizing data assets, TEKsystems strives to put forth a series of tools, technologies, and methodologies to meet customers in their current AWS cloud journey path. Innovation is key to achieve success, and we’ll highlight all areas where automation fast-tracks the initiative. In this post, we discuss a methodical approach to migrate an on-premises data warehouse solution such as Oracle or other flavors by moving to a cloud-native data warehouse like Amazon Redshift and achieve unparalleled scale while securing your business-critical data. Redshift’s semantics implementation can differ from other data warehouse solutions even for identical queries, ensuring queries return the correct data set while running on Redshift.Staff needs to retrain for ad-hoc data science interaction-solutioning to leverage existing client tools to work seamlessly with new cloud data warehouse.Upgrade of business intelligence (BI) and reporting infrastructure needed to leverage tools like Tableau or Amazon QuickSight to work with Redshift.Rewrite any data loader scripts that were coded in data export tools and other loader languages.Upgrade of ETL (extract, transform, load) infrastructure needed.Scale of the project-touching up millions of lines of SQL (no matter how miniscule the changes) turns any project into a non-scalable multi-year project.Typical challenges for such complex migrations include: This post will discuss the technical migration aspects for on-premises data warehouse solutions. Most enterprise customers are trying to migrate their on-premises data warehouse from Oracle or other on-premises solutions to Amazon Redshift. The difficulty primarily arises from the scale and complexity of the source environment, and only secondarily from functional discrepancy between source and destination system. How do you approach setting up a complex analytics platform involving billions of rows of data? It also required building strong data pipelines that are futuristic, scalable, and expandable to accommodate a variety of new sources, including point of sales (POS) and consumer sentiments. This necessitated an architectural overhaul of the customer’s legacy ways of collecting, processing, and storing key consumer and payments data. In the world of consumer goods, real-time insights drive competitive advantage and business value.Ī Fortune 500 global fast-moving consumer goods manufacturing company had the vision to maximize the benefit of analytics in decision support while increasing business growth and customer satisfaction opportunities. By Mahesh Pakala, Principal Solutions Architect – AWSīy Srinivasan Swaminatha, Managing Director, AWS Modern Cloud Analytics – TEKsystemsīy Lakshmanan Palaniappan, Sr.
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