Where (and How) to House Data

In many ways, data warehousing has changed a lot over the last three decades. There have been optimizations such as massively parallel processing (MPP), in-memory processing, vector processing, and columnar storage. The cloud, and its separation of storage and compute, was a breakthrough bringing forth additional capabilities such as autoscaling and elasticity, multi-cluster concurrency, and freedom from data warehouse physical appliance hardware. However, in other ways, data warehousing has not changed. The sequel to SQL turned out to be SQL. Throwing all your data into a Hadoop data lake did not obviate the need for the data warehouse, nor the need for the data quality and data governance that prevents data lakes from devolving into data swamps. Data lakehouses are in vogue now, but simply replacing HDFS with object storage in S3 buckets does not auto-magically make the data usable. Where (and how) to house data today and tomorrow requires not forgetting the lessons we have learned along the way. Join Howard Holton of GigaOm and Jim Harris of Vertica for a webinar discussion about the past, present, and future of data warehousing. Webinars attendees will also receive access to the related research GigaOm Radar for Data Warehouses, released in December 2022.