Reduce Data Lake Ingestion Time by 75% with Ingestion Factory

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It is estimated that data preparation eats up as much as 80% of data analysts’ time, leaving less bandwidth for actual analytics and significantly reducing a data lake’s return on investment. To dramatically speed up data ingestion and data preparation in the data lake, Zaloni offers a new solution, Ingestion Factory, which can help enterprises successfully hydrate and organize a production-ready data lake in weeks.

Zaloni’s Ingestion Factory provides the automation and repeatable processes needed for ‘industrial strength,’ high-volume data ingestion, cutting the effort it takes to define initial ingestion processes by 75-90% and making subsequent ingestion of similar data types essentially real-time. The solution applies and manages metadata as data is ingested into the lake – versus a data dump that requires additional steps to figure out what’s in the lake after the fact.

Zaloni’s Ingestion Factory provides a much-needed solution for enterprises looking for the most reliable, cost-effective way to do managed ingestion of data into the data lake,” said Scott Gidley, Zaloni’s VP of Product Management. “Zaloni Ingestion Factory does data ingestion the right way from the start.”

The solution combines Zaloni’s technology and services; businesses work with Zaloni experts to develop the architecture to move beyond a proof-of-concept (POC) to a production-ready data lake, and leverage Zaloni’s Bedrock Data Lake Management and Governance Platform for the metadata management, governance, security and privacy capabilities required for an enterprise-grade data lake.

Data-driven enterprises already know this: It takes too long to implement most DataOps technologies,” said Toph Whitmore, Blue Hill Research’s principal analyst for Big Data & Analytics. “Zaloni’s Ingestion Factory accelerates that too-often underestimated time from purchase to value recognition. It’s applied service model gets enterprise data operations up and running fast, and delivers the peace of mind that comes with a validated, best-practices approach to establishing a data lake.”

Key solution features

By simplifying and automating data lake ingestion and data preparation processes, Zaloni’s Ingestion Factory solution will make production-ready data lakes a reality for more businesses. The key features of Zaloni’s Ingestion Factory solution include:

  • High-volume, “industrial-strength” ingestion leveraging parallel ingestions; ingestion rate is customized to give clients the best ingestion throughput based on the cluster and network configuration
  • Automation of first-time and incremental ingestion of any data type from any data source across an organization into the data lake
  • For relational database management system (RDBMS) ingestion to Hadoop, Bedrock automatically creates Hive tables for each of the source RDBMS tables
  • “Ingestion accelerator” technology that enables repeatable and reliable ingestion processes
  • Application of metadata to the data upon ingestion for a managed data lake
  • Total visibility into the ingestion process to enable more “hands-off” IT management
  • Support for different refresh frequencies, including one-time historical load, full refresh, incremental refresh, and change data capture (CDC); Bedrock also supports slowly changing dimensions (SCDs) and SQL override options to load data

With the spread in adoption of data lake implementations, organizations are faced with the challenge to solve universal data challenges – fast ingestion of various data types and metadata management for big data environments.” said John L Myers, managing research director of analytics at Enterprise Management Associates, a Boulder, CO based industry analysis firm. “Zaloni enables organizations to meet this challenge and mature toward a ‘logical’ data lake architecture, which includes data sources beyond the boundaries of Hadoop to include data from the data warehouse and operational systems such as ERP and CRM.”


Zaloni offers tiered pricing for its Ingestion Factory solution, based on the complexity and quantity of data sources to be ingested.


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