In 2019, enterprise demands rose for real-time and near real-time analytics, and data continued to expand its role in everyday business operations and decision-making. Enterprises will continue to build on these trends in 2020, and that will drive analytics vendors to add new capabilities and expand their offerings. Here are eight key trends for analytics in 2020. In-memory costs are decreasing, and this will drive more analytics to real-time environments. The demand for real-time or near real-time analytics will require fast CPUs and in-memory processing.

There is much discussion these days about Lambda Architecture and its benefits for developing high performance analytic architectures. It offers a combination of a high performance, low latency ETL with a real-time layer, and a slower, more accurate, and flexible solution that runs in batch. As I work with it, I have learned to appreciate Cassandra's relative "immortality" and fit for such analytic systems. In a complex distributed system it's nice to know you have one component that you can rely on without much tending. Need to be highly available and regionally distributed?

There is much discussion these days about Lambda Architecture and its benefits for developing high performance analytic architectures. It offers a combination of a high performance, low latency ETL with a real-time layer, and a slower, more accurate, and flexible solution that runs in batch. As I work with it, I have learned to appreciate Cassandra's relative "immortality" and fit for such analytic systems. In a complex distributed system it's nice to know you have one component that you can rely on without much tending. Need to be highly available and regionally distributed?

There is much discussion these days about Lambda Architecture and its benefits for developing high performance analytic architectures. It offers a combination of a high performance, low latency ETL with a real-time layer, and a slower, more accurate, and flexible solution that runs in batch. As I work with it, I have learned to appreciate Cassandra's relative "immortality" and fit for such analytic systems. In a complex distributed system it's nice to know you have one component that you can rely on without much tending. Need to be highly available and regionally distributed?

There is much discussion these days about Lambda Architecture and its benefits for developing high performance analytic architectures. It offers a combination of a high performance, low latency ETL with a real-time layer, and a slower, more accurate, and flexible solution that runs in batch. As I work with it, I have learned to appreciate Cassandra's relative "immortality" and fit for such analytic systems. In a complex distributed system it's nice to know you have one component that you can rely on without much tending. Need to be highly available and regionally distributed?