The conference had over 6,000 attendees with over 150 sessions. There was no way I was able to attend all the sessions I was interested in. I tried to attend a sampling of sessions across the various tracks:
- Use Cases
- Advanced Operations
- Cassandra for Relational Developer
- Beginner Operations
- Tools, Internals & Theory
I can in no way summarize the entire summit; however, I want to highlight some of my key takeaways based on the sessions I attended.
Improving analytics capabilities
Relational data models support ad-hoc (e.g. unanticipated data access paths) and aggregate data analysis out of the box, typically at the expense of performance. NoSQL platforms gain performance benefits from a distributed architecture and data structures that are developed for certain data access paths. The inability to easily query Cassandra limited it's adoption to niche implementations. DataStax is mitigating this limitation by integrating Cassandra with other components that provide these capabilities. Specifically, Solr is used for ad-hoc search and Spark/Hadoop is used for aggregate analytics. This will pave the way for more general adoption of Cassandra.
Need to focus more on ROIMost of the content was technically focused and not business focused. General adoption of Cassandra and NoSQL tools in general will only happen when there is clear business value added. Next year I hope to see sessions about how to make a business case for adopting Cassandra and some uses that show clear ROI. There was a use case track; however, most of the sessions focused on lessons learned.
Enterprise hardeningRelational technology has been evolving since the 1970s. That is over 40 years of enterprise hardening (e.g. data management functions) or baggage, depending on whether you are a developer or data architect, that comes along with most RDMBs like Oracle, DB2 and Teradata. Most NoSQL platforms began life as developer-centric persistence platforms with minimal build in data management functions. This is changing as NoSQL platforms gain more general adoption. Functions like security, materialized views, transactions, consistency, management consoles, metadata management and standard APIs are being implement in NoSQL tools. I wonder how long it will be before they become bloated like the current RDBMS platforms!
Common Use Case ThemesThe two most common use cases for Big Data platforms are (1) reduction of operational licensing costs and (2) minimize time from data ingestion to analytics.
There is a huge push for SQL like interfaces to Big Data platforms because it makes them available to a much larger audience. CQL 3.0 provides a very familiar SQL like interface to Cassandra and Spark has Spark SQL which minimizes the need to know Scala or Java.