Accelerating RDBMS to JSON migration with no-code transforms

Enterprises around the world are looking to modernize their existing applications. They need to move their business applications to a more cost-effective, scalable, and flexible platform to leverage the true value of their data and achieve their business objectives. Whether their goals are business growth or fending off the competition, application modernization is the primary vehicle that will help them get there.

Often the first step in this process is to move data from an existing relational database repository (like Oracle, SQL Server, DB2, or Postgres, for example) into a JSON-based flexible database in the cloud (like MongoDB, Aerospike, Couchbase, Cassandra or DocumentDB). Sounds simple, right? I mean, really, if JSON (NoSQL) is so simple and flexible, why would data migration be hard? There must be a bunch of automated tools to facilitate this data migration, right?

Unfortunately, the answers are “Not really,” “Because data migration is rarely simple,” and “The available data migration tools are often DIY-based and don’t provide nearly the level of automation required to facilitate an ongoing, large-scale production-quality data migration.”

One of the first challenges is data modeling. To effectively leverage the benefits inherent in a JSON-based schema, you need to include data modeling as part of your migration strategy. Simply flattening or de-normalizing a relational schema into nested JSON structures, or worse yet, simply moving from relational to JSON without any data modeling consideration, results in a JSON data repository that is slow, inefficient, and difficult to query. You need an intelligent data modeling platform that automatically creates the most effective JSON structures based on your application needs and the target JSON repository without requiring specialized resources like data scientists and data engineers.

Once you’ve mapped the data, you need tools that allow you to build reliable, scalable data pipelines to move the data from the source to the target repository. Sadly, most of the tools available today are primarily DIY scripting tools that require both custom (often complex) coding to transform the data to the new schema properly and custom (often complex) monitoring to ensure that the new data pipelines are working reliably. You need a data pipeline automation and monitoring platform to move the data and ensure its quality.

This process of data transformation, pipeline automation, and monitoring are where most application modernization projects get bogged down and/or ultimately fail. These failed projects often consume significant resources before they fail, as well as affect the overall business functionality and outcomes, and lead to missed objectives.

Dataworkz provides built-in data pipeline monitoring that validates the data being transformed into the new JSON model. This means that the application modernization teams no longer need to get bogged down attempting to monitor and correct the incoming data streams. Dataworkz’s self-documenting lineage incorporates a detailed audit trail of the data transformations and any related exceptions.

The Dataworkz platform addresses the challenges in application modernization by accelerating the creation of high-quality, reliable, automated data pipelines with default and extensible no-code data transforms. Whether your data migration is a one-time effort or an ongoing continuous data collection and integration pipeline, Dataworkz provides a scalable, reliable, monitorable process that migrates your data and looks for data anomalies that can affect the overall functionality and quality of the data pipeline.

By integrating data modeling, no-code transformations, pipeline automation, and quality monitoring into a single, scalable platform, Dataworkz provides the solution needed to address the challenges that cause most projects to fail. Dataworkz directly addresses the “too many tools, not enough experts” problem that plagues application modernization projects today.

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