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Experts Share How to Capitalize on Your Data Lake


Data lake adoption is on the rise. Right now, 38% of DBTA subscribers have data lakes deployed to support data science, data discovery and real-time analytics initiatives, and another 20% are considering adoption.

Today, most data lakes are on-premises. However, the cloud is becoming an increasingly attractive location as well.

While data lakes have evolved and matured over the past few years of enterprise use, many challenges still exist.

DBTA recently held a webinar with Clive Bearman, director of product marketing, Attunity and Steve Wooledge, vice president of industry solutions, Arcadia Data, who discussed how data governance, security, integration and the ability to easily access and analyze information are all critical success factors for taking the data lake to the next level.

Taking a data lake to the next level depends on where you are in the business, where the tech is, and what is trying to be achieved, Bearman explained.

Enterprise data lake objectives haven’t changed. A data lakes is supposed to provide analytics-ready data in near real-time, gain agility to modify processes and technologies, and maintain or reduce the cost of traditional data warehousing.

But gaps do exist that are driving new requirements. Bearman introduced the Attunity for data lakes approach. This approach focuses on streaming data pipeline automation from ingestion to analytics-ready data sets. Attunity provides the following features:

  • Available for data architects and engineers
  • Rapidly deliver real-time and analytics-ready data
  • Remove the time, cost and risk of manual coding
  • Adaptable to new sources, targets, platforms, technologies

While data and the platforms that handle it have changed, business intelligence is getting left behind, according to Wooledge.

The next avenue that will evolve BI is “in-data lake BI.” According to a Forrester research paper “Upgrade Your BI Data Architecture for Agile Insights,” such in-data-lake BI architecture reduces LAN/WAN data movement; eliminates "choke points" like JDBC connectors; and enables BI applications, not just DBMSes, to be fully distributed.

Arcadia Data can provide a modern BI native to cloud and data lakes, Wooledge said. The platform provides:

  • Self-service: business users can easily analyze data.
  • In-data-lake BI: brings BI data in existing data platforms. Data never moves into a secondary proprietary system
  • IT-friendly: it’s secure, governed, fast, and centrally managed.

Enterprises can make data lakes analytics easier by looking for:

  • Faster data discovery: Search-based BI (natural language query)
  • Easier dashboard design: AI-driven visualization recommendations
  • Broader accessibility: AI-driven dashboard acceleration to reach 1000s of users

An archived on-demand replay of this webinar is available here.


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