From Static Data Warehouse to Scalable Insights and AI On-Demand
Government agencies today are dealing with a wider variety of data at a much larger scale. From satellite imagery to sensor data to citizen records, petabytes of semi- and unstructured data is collected each day. Unfortunately, traditional data warehouses are failing to provide government agencies with the capabilities they need to drive value out of their data in today’s big data world. In fact, 73% of federal IT managers report that their agency not only struggles with harnessing and securing data, but also faces challenges analyzing and interpreting it1.
Some of the most common pain points facing data teams in the federal government include:
- inelastic and costly compute and storage resources
- rigid architectures that require teams to build time-consuming ETL pipelines
- limited support for advanced analytics and machine learning
Fortunately, Databricks Unified Analytics Platform powered by Apache SparkTM provides a fast, simple, and scalable way to augment your existing data warehousing strategy by combining pluggable support for a broad set of data types and sources, scalable compute on-demand and the ability to perform low latency queries in real-time rather than investing in complicated and costly ETL pipelines. Additionally, Databricks provides the tools necessary for advanced analytics and machine learning, future proofing your analytics.
Join our three-part webinar series to learn:
- Part 1: How to build a modern data analytics solution for the Fed Government with Apache Spark and the Databricks Unified Analytics Platform
- Part 2: How to simplify ETL and change data capture (CDC) processes to support your data engineering and data science initiatives.
- Part 3: How to enrich your modern analytics platform with machine learning and deep learning
- Register for part one of our webinar series
- Learn how the Center for Medicare & Medicaid Services, Sevatec and other agencies are adopting Databricks to drive digital transformation