Apache Kylin : Implementing OLAP on the Hadoop platform

Explore the Adventure Works data set by building and analyzing an Apache Kylin cube from a Hive data warehouse; load data, create and troubleshoot cubes, and run analytical queries.

Prepare Adventure Works data for OLAP on Hadoop by importing into MySQL, transferring to HDFS with Sqoop, loading into Hive, and testing sample queries to guide design for Apache Kylin.

Master Apache Kylin for OLAP on the Hadoop platform by syncing Hive tables, creating a project, and loading data sources from Adventure Works while exploring cardinality in the model.

Build a fact table for internet sales and link customer and product dimensions. Use a star or snowflake schema with calendar year, product names, quantity, and sales amount.

Create the internet sales cube from the internet sales model in Apache Kylin, configure dimensions and measures, set derived versus normal dimensions, optimize aggregations, and prepare to build.

Submit a build request to create the cube and its segments, then monitor the job stages and logs as the cube constructs its cuboids.

the cube is ready to serve queries defined by the cube model, with queries run against the inside page's fact and dimension tables and grouped by year, month, and territory.

Identify common error types in Apache Kylin and fix hive query, mapreduce, and hive table errors using cailean hive config, job logs, and resume or discard options.

Analyze airline on-time performance with olap by building a cube from a year of flight delay data, using incremental refresh and a flat fact-dimension model for low-latency queries.

Prepare the flight delay dataset by downloading and extracting files, organizing into five Hive tables, then run Hive and Beeline scripts to create the database and transform data for analysis.

Load data sources with Hive, create a flight delay model, and build the cube. Incrementally refresh the cube using partitioned segments, merging weekly slices for performance.

Learn to perform incremental cube building with Apache Kylin on the Hadoop platform, set weekly date boundaries with inclusive first date and exclusive end date, and monitor submitted jobs.

Learn the single fact/dimension table model, where facts and dimensions reside in one flat table with no lookups, using the flat flight data example to build a cube.

Explore cube optimization and tuning in Apache Kylin, using aggregation groups, derived and joint dimensions, and hierarchical and mandatory dimensions to prune cuboid combinations and boost performance.

Advance cube optimization and tuning by configuring the broader system infrastructure, including dfs, the kubin engine, haji baz as the olap datastore, and hive as datastore and intermediate steering area.

Explore incremental refresh for cubes on star, snowflake, or single-table schemas, with segment refresh, purge actions, and practical cube optimization guided by use case.

Explore how Apache Kylin enables OLAP on streaming data by building cube segments for each time slot from a Kafka stream, answering questions about peak hits and hourly patterns.

Set up Kafka brokers, confirm they are running, and create a test topic. Run the log file reader simulation to transform lines into JSON and publish to the topic.

Learn to implement an olap cube over streaming data in kafka with Kylin by creating a streaming table from a topic, defining dimensions and partitions, and building a clickstream cube.

Build the clickstream OLAP cube from streaming data using micro batches and segments, with concurrent jobs and REST API automation. Compare layer vs in-memory cube algorithms and manage partitions.

Query the built cube in the Apache Kylin OLAP setup to count hits, measure size, and drill by month, day, hour, and status codes.

Troubleshoot common Apache Kylin streaming issues by fixing coffee dependency and configuring Kafka home, verifying correct topic and offsets, and using the Kafka console consumer to confirm data.

Explore Apache Kylin client integration for querying Hadoop-based OLAP data, covering human and system clients, RESTful web services, drivers, and notebook tools to share results.

Learn to connect with Apache Kylin via RESTful APIs to manage models and cubes, perform analytical sql queries, monitor jobs, and integrate with external schedulers and web apps.

Explore configuring the ODBC driver to connect Apache Kylin OLAP cubes with MS Excel, including DSN setup in Windows control panel and first queries with charts and pivot tables.

Connect to the Kailin JDBC driver from a simple Java application, add the driver to the class path with Maven, and execute a query.

Learn to integrate Apache Kylin with Apache Zeppelin by installing and configuring the Kailin interpreter, creating notebooks, and building interactive SQL visualizations for customer and sales analysis.

Examine query routing in Apache Kylin, ensuring queries match the cube model, and use JDBC pushdown to route non-matching queries to Hive or other engines, reducing latency.

Explore offline storage cleanup in Apache Kylin: identify and drop intermediate hive tables, hash tables, and dfs parts with the storage cleanup tool to tidy the environment.

Apply Apache Kylin to a production OLAP use case in your Hadoop cluster, using it as an end-to-end query service to keep data in place and improve user experience.