What are the most common real-time analytics tools for data warehousing?

2 In-Memory Computing

In-memory computing is a technique that stores and processes data in the main memory of servers, rather than on disk or in external databases. This approach can provide faster access and analysis of data, as well as lower costs and complexity of data management. Examples of in-memory computing tools include Redis, Memcached, Apache Ignite, and SAP HANA. In-memory computing can enable real-time analytics for data warehousing by improving query performance and response time, reducing data movement and disk I/O, supporting high availability and scalability of data, and enabling advanced analytics and machine learning on large data sets. However, this approach may also come with some challenges such as increasing memory requirements and costs, ensuring data persistence and durability, handling data security and encryption , and managing data distribution and synchronization.

3 Lambda Architecture

Lambda architecture is a design pattern that combines batch and stream processing to handle both historical and real-time data. This architecture consists of three layers: the batch layer, the speed layer, and the serving layer. The batch layer processes historical data in batches and stores the results in a master data set, while the speed layer processes streaming data in real time and stores the results in a fast data set. The serving layer then merges the master and fast data sets, creating a unified view for queries. Examples of tools used for lambda architecture include Apache Hadoop, Apache Spark, Apache Kafka, and Apache Cassandra. This architecture can enable real-time analytics for data warehousing by balancing latency and accuracy of data processing, supporting both batch and stream analytics on the same data, providing a consistent view of data, and handling large and complex data sets. However, it can also pose some challenges such as increasing complexity and maintenance of multiple layers and tools, managing data duplication and consistency across layers, handling updates and deletes of data, as well as tuning performance and resource allocation for different layers.

4 Kappa Architecture

Kappa architecture is a simplified version of lambda architecture that eliminates the batch layer and relies only on stream processing. It consists of two layers: the stream layer and the serving layer, which processes all data as streams and stores and serves the results of the stream layer for queries respectively. Some tools used for kappa architecture are Apache Kafka, Apache Spark, Apache Flink, and Elasticsearch. This type of architecture can enable real-time analytics for data warehousing by reducing complexity and overhead of data processing, eliminating data duplication and inconsistency across layers, supporting incremental and continuous updates of data, and enabling faster and simpler development and deployment of analytics applications. However, there are some challenges associated with this architecture such as needing reliable and scalable stream processing tools, handling out-of-order, duplicate, or missing data, supporting complex event processing and pattern detection on streaming data, and managing stateful computations and state management.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?