Must-Haves for Modern Data Warehousing

To fit into modern analytics ecosystems, legacy data warehouses must evolve—both architecturally and technologically—to deliver the agility, scalability, and flexibility that business need to thrive in today’s data-driven economy.

Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to Hadoop and Spark, and machine learning and automation.

DBTA held a webinar with Clive Bearman, director of content, product and marketing strategy, Qlik; David Leichner, CMO, SQream; and Felipe Hoffa, data cloud advocate, Snowflake who discussed the must-have capabilities for modern data warehousing.

Users historically had performance assumptions for their data warehouses such as limited, fixed resources, having to tune for performance, and do manual upkeep, Hoffa said. Traditional data architecture is complex, costly and constrained.

Hoffa’s requirements for a modern data warehouse include:

  • One platform one copy of data, many workloads
  • Secure and governed access to all data
  • Near-zero maintenance, as a service
  • Unlimited performance and scale

According to Leichner, the most common struggles organizations are dealing with from ingest to insights include queries running way too long and sometimes not at all, lots of data but can only analyze 10% of it, lengthy data preparation, and more.

Bearman outlined his must-have tips for modern data warehousing including:

  • Align with your organizations top strategic initiatives or projects
  • Automate where possible
  • Architect for change
  • Leverage a data catalog

Leichner recommended the Sqream platform which is built for massive data analytics. It is powered by GPUs, massively scalable, a SQL database, extensible for machine learning and AI, provides a minimal footprint, and is lightning fast.

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