
Install and run a standalone Apache Druid deployment locally, access the Druid portal, and explore broker, coordinator, historical, middle manager, and router for real-time queries with ingestion options.
Explore evolving the druid schema by adding manager_id and department_id, configuring schema, re-ingesting via kafka, and applying updates with minimal downtime.
Explore Apache Druid batch ingestion, including native, SQL, and Hadoop-based options, and learn to automate ingestion with REST APIs and tune parallelism to accelerate ETL loads.
Learn batch SQL ingestion in Apache Druid using HTTP APIs, with controller and worker tasks in multi-stage workflow, ingesting CSV data from S3 and creating tables with partitioning and clustering.
Access Druid tables from Spark and create a Spark DataFrame with Avatica JDBC. Learn to configure dependencies and validate predicate pushdown and aggregation.
Explore how Apache Druid uses Confluent Schema Registry to validate Avro records from Kafka, ensuring schema compatibility for producers and consumers.
Learn to query Druid data from Hive by creating an external Druid table linked to tweets, use explain to inspect the plan, and push queries down to Druid with Druid.query.json.
Explore how the Presto Druid Connector enables querying Druid data and accessing Druid tables, and compare PrestoDB and Trino through setup, ingestion, and PrestoCLI queries.
Install and run Presto on your local machine, connect to a Druid cluster via the druid connector, and query Druid data using the Presto CLI with pushdown enabled.
What do you learn from this course ?
In this course, we learn end-to-end apache druid salient features and integration with Apache Hive, Prestodb (Trino), Spark and Schema Registry one by one.
We start this course by gaining theoretical knowledge on Druid and its key features. We write our own Twitter Producer app which pulls the tweets from twitter in realtime and push the tweets to apache Kafka. We create a Kafka streaming task on Druid which pull tweets from Kafka and store it into Apache Druid. Also, we learn how to apply transformation, filter, schema configuration, tuning during kafka ingestion.
In the 3rd module, we explore Native and SQL Batch ingestion methods in depth. In an ETL pipeline after extract and transform step, if you want to load the dataset to druid, then you must checkout this section. We will automate the entire loading to druid part.
In the 4th module, we learn how to read druid tables using Spark and create Spark Dataframe from it. We also explore the predicate and aggregate pushdown spark features.
Section 5, talk about the Schema Registry. We learn how druid talk to schema registry and achieve the schema validation. Also how druid parse the Avro records.
Section 6 & 7 exposes the out of box druid capabilities. which are hive and presto integration. If your organisation data resides in hive or presto and you would like to join with druid table, then you should accomplish hive or presto integration.