To fully leverage your data, the initial step involves consolidating all your data and selecting the desired columns for your BI Dashboard, such as Cognos Analytics or Planning Analytics, or preparing your training dataset to build your Machine Learning or Deep Learning models.
However, the challenge lies in gathering scattered data from diverse tables, files, or cloud platforms. Data is often fragmented across multiple silos, hindering real-time access. To address this issue, watsonx.data emerges as the optimal data lakehouse platform, eradicating data silos and facilitating real-time access. This platform seamlessly facilitates the access and processing of data from various sources using SQL in a centralized location. Consequently, merging datasets stored in different databases or buckets becomes a straightforward task within the watsonx.data platform.
Traditionally, data scientists and engineers grapple with accessing different environments, configuring computing machines, and navigating varied query syntax for dataset preparation—resulting in a time-intensive process. Achieving real-time data access proves challenging, incurring significant labor, time, and cost.
watsonx.data revolutionizes this by enabling federated queries on your data without the need for complex ETL processes, employing a unified SQL syntax. This approach provides real-time access to data in a singular location, significantly saving time, costs, and labor.
For instance, consider the following example of crafting a SQL federated query within watsonx.data, seamlessly merging datasets from various tables and catalogs across Postgres Database, DB2 Database, and Hive Buckets. This eliminates the necessity for ETL processes, streamlining access to diverse systems with disparate query syntax and ensuring prompt data retrieval.
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