Kudu is designed to fill the gap between HDFS and HBase. However, designing a Kudu-based cluster presents a number of challenges. Wei Chen and Zhaojuan Bian share a real-world use case from the automobile industry to explain how to design a Kudu-based E2E system. They also discuss key indicators to tune Kudu and OS parameters and how to select the best hardware components for different scenarios.

Big data architectures and enterprise/microservice architectures are slowly converging. Big data is transitioning to "fast data," emphasizing streaming over batch processing, while data processing is growing ubiquitous. Dean Wampler explores the SMACK stackSpark, Mesos, Akka, Cassandra, and Kafkaand explains how it addresses the needs of both fast data and the enterprise.

Sridhar Alla and Kiran Muglurmath explain how real-time analytics on Comcast Xfinity set-top boxes (STBs) help drive several customer-facing and internal data-science-oriented applications and how Comcast uses Kudu to fill the gaps in batch and real-time storage and computation needs, allowing Comcast to process the high-speed data without the elaborate solutions needed till now.

The self-service YP Analytics application allows advertisers to understand their digital presence and ROI. Richard Langlois explains how Yellow Pages used this expertise for an internal use case that delivers real-time analytics with Tableau, using OLAP on Hadoop and enabled by its stack, which includes HDFS, Parquet, Hive, Impala, and AtScale, for fast, real-time analytics and data exploration.

Apache Eagle is an open source monitoring solution to instantly identify access to sensitive data, recognize malicious activities, and take action. Arun Karthick Manoharan, Edward Zhang, and Chaitali Gupta explain how Eagle helps secure a Hadoop cluster using policy-based and machine-learning user-profile-based detection and alerting.

Ruhollah Farchtchi explores best practices for building systems that support ad hoc queries over real-time data and offers an overview of Kudu, a new storage layer for Hadoop that is specifically designed for use cases that require fast analytics on rapidly changing data with a simultaneous combination of sequential and random reads and writes.

Currently, multitenancy in Hadoop is limited to organizations running separate Hadoop clusters, and the secure sharing of resources is achieved using virtualization or containers. Jim Dowling describes how HopsWorks enables organizations to securely share a single Hadoop cluster using projects and a new metadata layer that enables protection domains while still allowing data sharing.

Real-time analysis starts with transforming raw data into structured records. Typically this is done with bespoke business logic custom written for each use case. Joey Echeverria presents a configuration-based, reusable library for data transformation that can be embedded in real-time stream-processing systems and demonstrates its real-world use cases with Apache Kafka and Apache Hadoop.

Ted Malaska and Jeff Holoman explain how to go from zero to full-on time series and mutable-profile systems in 40 minutes. Ted and Jeff cover code examples of ingestion from Kafka and Spark Streaming and access through SQL, Spark, and Spark SQL to explore the underlying theories and design patterns that will be common for most solutions with Kudu.

Developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda. Helena Edelson and Evan Chan highlight a much simpler approach using the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka) plus FiloDB, a new entrant to the distributed-database world, which combines streaming and ad hoc analytics.