Architecting a next-generation data platform

Description

Rapid advancements are causing a dramatic evolution in both the storage and processing capabilities in the open source big data software ecosystem. These advancements include projects like:

• Apache Kudu, modern columnar data store that complements HDFS and Apache HBase by offering efficient analytical capabilities and fast inserts and updates with Hadoop;
• Apache Kafka, which provides a high-throughput and highly reliable distributed message transport;
• Apache Spark, which is rapidly replacing parallel processing frameworks such as MapReduce due to its efficient design and optimized use of memory. Spark components such as Spark Streaming and Spark SQL provide powerful near real-time processing;
• Distributed storage systems, such as HDFS and Cassandra;
• Parallel query engines such as Apache Impala CockroadDB, which provide capabilities for highly parallel and concurrent analysis of datasets.

These storage and processing systems provide a powerful platform to implement data processing applications on batch and streaming data. While these advancements are exciting, they also add a new array of tools that architects and developers need to understand when architecting modern data processing solutions.

Using Customer 360 and the IoT as examples, Jonathan Seidman and Ted Malaska explain how to architect a modern, real-time big data platform leveraging components to reliably integrate multiple data sources, perform real-time and batch data processing, reliably store massive volumes of data, and efficiently query and process large datasets. Along the way, they discuss considerations and best practices for utilizing these components to implement solutions, cover common challenges and how to address them, and provide practical advice for building your own modern, real-time data architectures.

Topics include:

• Accelerating data processing tasks such as ETL and data analytics by building near real-time data pipelines using modern open source data integration and processing components
• Building reliable and efficient data pipelines, starting with source data and ending with fully processed datasets
• Providing users with fast analytics on data using modern storage and query engines
• Leveraging these capabilities along with other tools to provide sophisticated machine learning and analytical capabilities for users