Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Architecting a next-generation data platform

Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
13:3017:00 Tuesday, 22 May 2018
Data engineering and architecture
Location: Capital Suite 12 Level: Advanced
Secondary topics:  Data Platforms
Average rating: ****.
(4.33, 3 ratings)

Who is this presentation for?

  • Software engineers, software architects, technical leads, project managers, and data engineers

Prerequisite knowledge

  • An understanding of Hadoop concepts and the Hadoop ecosystem, traditional data management systems (e.g., relational databases), and programming languages and concepts

What you'll learn

  • Discover how new and existing tools in the Hadoop ecosystem can be integrated to implement new types of data processing and analysis
  • Learn considerations and best practices for implementing these applications


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
Photo of Ted Malaska

Ted Malaska

Capital One

Ted Malaska is a director of enterprise architecture at Capital One. Previously, he was the director of engineering in the Global Insight Department at Blizzard; principal solutions architect at Cloudera, helping clients find success with the Hadoop ecosystem; and a lead architect at the Financial Industry Regulatory Authority (FINRA). He has contributed code to Apache Flume, Apache Avro, Apache Yarn, Apache HDFS, Apache Spark, Apache Sqoop, and many more. Ted is a coauthor of Hadoop Application Architectures, a frequent speaker at many conferences, and a frequent blogger on data architectures.

Photo of Jonathan Seidman

Jonathan Seidman


Jonathan Seidman is a software engineer on the cloud team at Cloudera. Previously, he was a lead engineer on the big data team at Orbitz, helping to build out the Hadoop clusters supporting the data storage and analysis needs of one of the most heavily trafficked sites on the internet. Jonathan is a cofounder of the Chicago Hadoop User Group and the Chicago Big Data Meetup and a frequent speaker on Hadoop and big data at industry conferences such as Hadoop World, Strata, and OSCON. Jonathan is the coauthor of Hadoop Application Architectures from O’Reilly.