Streaming
Traditional data warehouses support batch analytic queries. However, in the open source ecosystem as well as in commercial products, we are seeing a convergence of hybrid batch and streaming architectures. For example, Spark supports both batch processing as well as stream processing with Spark Streaming. Apache Flink is another project aiming to combine batch and stream processing. This is a natural progression because fundamentally it is possible to use very similar APIs and languages to specify a batch or streaming computation. It is no longer necessary to have two completely disparate systems. In fact, a unified architecture makes it easier to discover different types of data sources.
Hybrid batch and streaming architectures will also prove to be extremely beneficial when it comes to IoT data. Streaming can be used to analyze and react to data in real time as well as to ingest data into the data lake for batch processing. Modern, high-performance messaging systems such as Apache Kafka can be used to help in the unification of batch and streaming. Integration tools can help feed Kafka, process Kafka data in a streaming fashion, and also feed a data lake with filtered and aggregated data.
Data Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management Requirements
2016 is the year of the data lake. It will surround and, in some cases, drown the data warehouse, and we'll see significant technology innovations, methodologies and reference architectures that turn the promise of broader data access and Big Data insights into a reality. But Big Data solutions must mature and go beyond the role of being primarily developer tools for highly skilled programmers. The enterprise data lake will allow organizations to track, manage and leverage data they've never had access to in the past. New data management strategies are already leading to more predictive and prescriptive analytics that are driving improved customer-service experiences, cost savings and an overall competitive advantage when there is the right alignment with key business initiatives.
So whether your enterprise data warehouse is on life support or moving into maintenance mode, it will most likely continue to do what it's good at for the time being: operational and historical reporting and analysis (a.k.a. rear-view mirror).
As you consider adopting an enterprise data lake strategy to manage more dynamic, poly-structured data, your data integration strategy must also evolve to handle the new requirements. Thinking that you can simply hire more developers to write code or rely on your legacy rows-and-columns-centric tools is a recipe to sink in a data swamp instead of swimming in a data lake. In this slideshow, Craig Stewart, VP product management at SnapLogic, has identified eight enterprise data management requirements that must be addressed in order to get maximum value from your Big Data technology investments.
