Mark Needham

Real-Time Data Analytics is becoming very popular these days because of the important values and insights that it is bringing to the business. It also relies on super-fast data ingestion and that is all possible by leveraging on a powerful event-driven streaming platform.

Apache Kafka forms the backbone of the modern data pipeline and its stream processing capabilities provide insights on events as they arrive. But what if we want to go further than this and execute analytical queries on this real-time data. The OLAP databases used for analytical workloads traditionally executed queries on yesterday's data with query latency in the 10s of seconds. The emergence of real-time analytics has changed all this and the expectation is that we should now be able to run thousand of queries per second on fresh data with query latencies typically seen on OLTP databases. This is where Apache Pinot comes into the picture. Apache Pinot is a realtime distributed OLAP datastore, which is used to deliver scalable real time analytics with low latency. It can ingest data from streaming sources like Kafka, as well as from batch data sources (S3, HDFS, Azure Data Lake, Google Cloud Storage), and provides a layer of indexing techniques that can be used to maximise the performance of queries. Come to this talk to learn how you can add real-time analytics capability to your data pipeline.