Buyers Guide and Check List for Data Integration
Snapshot: The data integration landscape today
Gone are the days of sole batch ETL (extract, transform, and load) with the help of a few skilled developers fulfilling data integration requirements. A new dynamic and fluid model of data integration has taken its place – bringing data from across a business to users when and how they need it. Much of the changed approach is driven by a broader diversity of cloud data consumption models, as well as a surge in the number and types of applications demanding real-time data delivery.
Cloud delivery models and applications have become part of every organization’s strategic business strategy, allowing them to expand capabilities, reduce costs, and drive digital transformation. However, bringing the right data from existing infrastructure to the cloud for business consumption can be an impossible task for many organizations. Cloud, and the benefits that it promises, requires a new way of thinking about data integration.
As a data integration leader in your organization, you can leverage moving to cloud as an opportunity to modernize existing integration approaches. To modernize, you cannot merely “copy” and “paste” existing data integration pipelines – traditional data pipelines are not adaptive enough. Instead, looking for tools that can build links between existing infrastructure and new cloud investments in an environment-agnostic, future-proof way is imperative. You need only look at what’s transpired over the past ten years to understand why.
The rise of Hadoop and its broad array of on-premises offerings helped organizations to replace their traditional enterprise data warehouses. The early 2010s saw MapReduce fall to Spark and the introduction of an entirely new programming paradigm altogether. Shortly after that, a variety of managed Spark offerings from vendors like Databricks, AWS, Azure, and Google Cloud Platform promised improved agility and scalability at lower TCO. Successfully navigating these waves of revolution requires software that allows you to build integration frameworks that work seamlessly across multiple environments.
The rise of the cloud data warehouse has brought with it promises of unlimited scalability, unrivaled user concurrency, zero administrative overhead, and improved data sharing across the organization. Delivering on these promises requires data pipelines to provide access to information around the business, including legacy systems such as mainframe and IBM i. As a result, investing in data integration software that can natively connect to both cloud and legacy sources is imperative.