The Hadoop Ecosystem Table

• GGFS allows read-through and write-through to/from underlying HDFS or any other Hadoop compliant file system with zero code change. Essentially GGFS entirely removes ETL step from integration.
• GGFS has ability to pick and choose what folders stay in memory, what folders stay on disc, and what folders get synchronized with underlying (HD)FS either synchronously or asynchronously.
• GridGain is working on adding native MapReduce component which will provide native complete Hadoop integration without changes in API, like Spark currently forces you to do. Essentially GridGain MR+GGFS will allow to bring Hadoop completely or partially in-memory in Plug-n-Play fashion without any API changes.

The Apache Apex platform is supplemented by Apache Apex-Malhar, which is a library of operators that implement common business logic functions needed by customers who want to quickly develop applications. These operators provide access to HDFS, S3, NFS, FTP, and other file systems; Kafka, ActiveMQ, RabbitMQ, JMS, and other message systems; MySql, Cassandra, MongoDB, Redis, HBase, CouchDB and other databases along with JDBC connectors. The library also includes a host of other common business logic patterns that help users to significantly reduce the time it takes to go into production. Ease of integration with all other big data technologies is one of the primary missions of Apache Apex-Malhar.

Apex, available on GitHub, is the core technology upon which DataTorrent's commercial offering, DataTorrent RTS 3, along with other technology such as a data ingestion tool called dtIngest, are based.

• Centralized Control Flow: Apache REEF turns the chaos of a distributed application into events in a single machine, the Job Driver. Events include container allocation, Task launch, completion and failure. For failures, Apache REEF makes every effort of making the actual `Exception` thrown by the Task available to the Driver.
• Task runtime: Apache REEF provides a Task runtime called Evaluator. Evaluators are instantiated in every container of a REEF application. Evaluators can keep data in memory in between Tasks, which enables efficient pipelines on REEF.
• Support for multiple resource managers: Apache REEF applications are portable to any supported resource manager with minimal effort. Further, new resource managers are easy to support in REEF.
• .NET and Java API: Apache REEF is the only API to write YARN or Mesos applications in .NET. Further, a single REEF application is free to mix and match Tasks written for .NET or Java.
• Plugins: Apache REEF allows for plugins (called "Services") to augment its feature set without adding bloat to the core. REEF includes many Services, such as a name-based communications between Tasks MPI-inspired group communications (Broadcast, Reduce, Gather, ...) and data ingress.

The model behind Beam evolved from a number of internal Google data processing projects, including MapReduce, FlumeJava, and Millwheel. This model was originally known as the “Dataflow Model” and first implemented as Google Cloud Dataflow, including a Java SDK on GitHub for writing pipelines and fully managed service for executing them on Google Cloud Platform.

In January 2016, Google and a number of partners submitted the Dataflow Programming Model and SDKs portion as an Apache Incubator Proposal, under the name Apache Beam (unified Batch + strEAM processing).

• Relational data model (tables) with strongly-typed columns and a fast, online alter table operation.
• Scale-out and sharded with support for partitioning based on key ranges and/or hashing.
• Fault-tolerant and consistent due to its implementation of Raft consensus.
• Supported by Apache Impala and Apache Drill, enabling fast SQL reads and writes through those systems.
• Integrates with MapReduce and Spark.
• Additionally provides "NoSQL" APIs in Java, Python, and C++.

• in Map-Reduce mode using Apache Hadoop,
• in BSP mode (Bulk Synchronous Parallel mode) using Apache Hama, and
• in Spark mode using Apache Spark.
• in Flink mode using Apache Flink.

H2O is a statistical, machine learning and math runtime tool for bigdata analysis. Developed by the predictive analytics company H2O.ai, H2O has established a leadership in the ML scene together with R and Databricks’ Spark. According to the team,  H2O is the world’s fastest in-memory platform for machine learning and predictive analytics on big data. It is designed to help users scale machine learning, math, and statistics over large datasets.

In addition to H2O’s point and click Web-UI, its REST API allows easy integration into various clients. This means explorative analysis of data can be done in a typical fashion in R, Python, and Scala; and entire workflows can be written up as automated scripts.

Sparkling Water combines two open source technologies: Apache Spark and H2O - a machine learning engine.  It makes H2O’s library of Advanced Algorithms including Deep Learning, GLM, GBM, KMeans, PCA, and Random Forest accessible from Spark workflows. Spark users are provided with the options to select the best features from either platforms to meet their Machine Learning needs.  Users can combine Sparks’ RDD API and Spark MLLib with H2O’s machine learning algorithms, or use H2O independent of Spark in the model building process and post-process the results in Spark.

Sparkling Water provides a transparent integration of H2O’s framework and data structures into Spark’s RDD-based environment by sharing the same execution space as well as providing a RDD-like API for H2O data structures.

Apache SystemML was open sourced by IBM and it's pretty related with Apache Spark. If you thinking in Apache Spark as the analytics operating system for any application that taps into huge volumes of streaming data. MLLib, the machine learning library for Spark, provides developers with a rich set of machine learning algorithms. And SystemML enables developers to translate those algorithms so they can easily digest different kinds of data and to run on different kinds of computers.

SystemML scales for big data analytics with high performance optimizer technology, and empowers users to write customized machine learning algorithms using simple, domain-specific language (DSL) without learning complicated distributed programming. It is an extensible complement framework of Spark MLlib.

However The Apache Yetus project addresses much more than the traditional patch testing, it's a better approach including a massive rewrite of the patch testing facility used in Hadoop.

Cloudbreak leverages the cloud infrastructure platforms to create host instances, uses Docker technology to deploy the requisite containers cloud-agnostically, and uses Apache Ambari (via Ambari Blueprints) to install and manage a Hortonworks cluster. This is a tool within the HDP ecosystem.