Big Data refers to the new technologies and applications introduced to handle increasing Volume, Velocity and Variety of data while enhancing data utilization capabilities such as Variability, Veracity, and Value.
The large Volume of data available forces horizontal scalability of storage and processing and has implications for all the other V-attributes. The increasing velocity of data ingestion and change implies the need for stream processing, filtering, and processing optimizations. The variety of data types (e.g. multimedia) being generated a requires the use of non-traditional data stores and processing.
| Category | Project / Component | Description | Link | Built through Bigtop | Built and Ported | Upstream Build | CI | Smoke tests complete | Testing in Clustered environment |
|---|---|---|---|---|---|---|---|---|---|
| Core Components | |||||||||
| Apache Bigtop | Bigtop is a project for Infrastructure Engineers and Data Scientists looking for comprehensive packaging, testing, and configuration of the leading open source big data components. List of projects/components included in it are here. It packages RPMs and DEBs and also produces provides vagrant recipes, raw images and docker images. | JIRA |
|  | | link | |||
| Apache Hadoop | Hadoop is a framework that allows for distributed processing of large data sets across clusters of computers using simple programming models. It includes HDFS, MapReduce, YARN HCatalog | JIRA |
RPM DEB | |
Tarball | ||||
| Operational Components | |||||||||
| Apache Ambari | The Apache Ambari project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. Ambari provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs. | JIRA |
RPM DEB | |  | ||||
| Apache Zookeeper | ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. | JIRA |
RPM DEB | | |||||
| Apache Oozie | Oozie is a workflow scheduler system to manage Apache Hadoop jobs. | | |||||||
| Streaming Components | |||||||||
| Apache Spark | Apache Spark is a unified analytics engine for large-scale data processing. |
RPM DEB | |
Tarball | |||||
| Apache Tez | TEZ project is aimed at building an application framework which allows for a complex directed-acyclic-graph of tasks for processing data |
RPM DEB | | ||||||
| Apache Flink | Apache Flink is a stream processing framework for distributed, high-performing, always-available, and accurate data streaming applications. |
RPM DEB | | | |||||
| Apache Kafka | Apache Kafka is a distributed streaming platform capable of handling trillions of events a day. Initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log |
RPM DEB | | | |||||
| Apache Beam | Apache Beam is an unified programming model to define and execute data processing pipelines, including ETL, batch and stream (continuous) processing | | | ||||||
| Apache Flume | Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data. |
RPM DEB | | ||||||
| Apache Storm | Apache Storm is a distributed stream processing computation framework written predominantly in the Clojure programming language. | ||||||||
| Apache NiFi | Apache NiFi (short for NiagaraFiles) supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic | | | ||||||
| Apache MiNiFi | Apache NiFi - MiNiFi is a complementary data collection approach that supplements the core tenets of NiFi in dataflow management, focusing on the collection of data at the source of its creation. | | | ||||||
| Apache Apex | Apache Apex is a unified platform for big data stream and batch processing. Use cases include ingestion, ETL, real-time analytics, alerts and real-time actions. |
RPM DEB | | ||||||
| Database and Data Warehousing Components | |||||||||
| Apache Hive | Hive is a data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage and queried using SQL syntax. |
RPM DEB | | ||||||
| Apache HBase | HBase is an open-source, non-relational, distributed database modeled after Google's Bigtable |
RPM DEB | | ||||||
| SparkSQL | Spark SQL is a Spark module for structured data processing |
RPM DEB | | ||||||
| Apache Pig | Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. |
RPM DEB | | ||||||
| Apache Pheonix | Apache Phoenix is a massively parallel, relational database engine supporting OLTP for Hadoop using Apache HBase as its backing store. | ||||||||
| Apache Impala | Apache Impala is a query engine and native analytic database for Apache Hadoop. Impala is shipped by Cloudera, MapR, Oracle, and Amazon | ||||||||
| Apache Drill | Apache Drill is a framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets | ||||||||
| PrestoDB | Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. | ||||||||
| Apache Cassandra | Apache Cassandra is a massively scalable open source non-relational database that offers continuous availability, linear scale performance, operational simplicity and easy data distribution across multiple data centers and cloud availability zones. | | |||||||
| ScyllaDB | Scylla is a distributed NoSQL data store. It supports the same protocols as Cassandra (CQL and Thrift) and the same file formats (SSTable), but is a completely rewritten implementation, using the C++14 language replacing Cassandra's Java, and replacing threads, shared memory, mapped files, and other classic Linux programming techniques. | ||||||||
| Apache Ignite | Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale with support for JCache, SQL99, ACID transactions, and machine learning. |
RPM DEB | | ||||||
| Apache Alluxio | Alluxio, formerly Tachyon, enables any application to interact with any data from any storage system at memory speed. |
RPM DEB | | ||||||
| Postgres | Postgres is an object-relational database management system (ORDBMS) with an emphasis on extensibility and standards compliance. | ||||||||
| Memcached | Memcached is a high performance multi-threaded event-based in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering. | | |||||||
| Apache Sqoop | Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases. |
RPM DEB | | ||||||
| Apache Falcon | Falcon is a feed processing and feed management system aimed at making it easier for end consumers to onboard their feed processing and feed management on hadoop clusters | ||||||||
| Redis | Redis is a in-memory data structure store, used as a database, cache and message broker. | ||||||||
| Apache Kudu | Kudu provides a combination of fast inserts/updates and efficient columnar scans to enable multiple real-time analytic workloads across a single storage layer. As a new complement to HDFS and Apache HBase, Kudu gives architects the flexibility to address a wider variety of use cases without exotic workarounds. | ||||||||
| Governance and Security | |||||||||
| Apache Ranger | Apache Ranger is a framework to enable, monitor and manage comprehensive data security across the Hadoop platform. | ||||||||
| Apache Atlas | Atlas is a scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem. | ||||||||
| Apache Knox | The Apache Knox Gateway (“Knox”) provides perimeter security so that the enterprise can confidently extend Hadoop access to more of those new users while also maintaining compliance with enterprise security policies | ||||||||
| Apache Sentry | Apache Sentry is a system for enforcing fine grained role based authorization to data and metadata stored on a Hadoop cluster. | ||||||||
| File Formats | |||||||||
| Apache Avro | Avro is a remote procedure call and data serialization framework developed within Apache's Hadoop project. | ||||||||
| Parquet | Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language. | ||||||||
| ORC | The Optimized Row Columnar (ORC) file format provides a highly efficient way to store Hive data. It was designed to overcome limitations of the other Hive file formats. Using ORC files improves performance when Hive is reading, writing, and processing data | ||||||||
| RC | RCFile (Record Columnar File) is a data placement structure that determines how to store relational tables on computer clusters | ||||||||
| Data Science Notebooks | |||||||||
| Jupyter | The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. | ||||||||
| Zeppelin | Zeppelin is a Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more |
RPM DEB | | ||||||
| Analytics Components | |||||||||
| Apache Arrow | Apache Arrow is a development platform for in-memory analytics. | ||||||||
| Apache Solr | Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites. | | |||||||
| Elastic Stack | The Elastic Stack or ELK contains the following components - ElasticSearch, LogStash, Kibana and Beats. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch. | | |||||||
| Ganglia | Ganglia is a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids. | ||||||||
| Machine Learning | |||||||||
| Spark MLib | MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. |
RPM DEB | | ||||||
| Mahout | Apache Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop and using the MapReduce paradigm. |
RPM DEB | | ||||||
| H2O | Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...) | | |||||||
| TensorFlow | TensorFlow is a software library for numerical computation using data flow graphs. | | | ||||||
| Dependencies | |||||||||
| Smoke Tests | |||||||||
| Benchmarking | |||||||||
| TPC Benchmarking Tools | TPC Benchmarks consists of TPC-C, TPC-DI, TPC-DS, TPC-E, TPC-H, TPCx-BB, TPCx-HS, TPCx-V benchmarking tools | ||||||||
| Big Bench | The BigBench specification comprises two key components: a data model specification, and a workload/query specification. | ||||||||
| HiBench | HiBench is a big data benchmark suite that helps evaluate different big data frameworks in terms of speed, throughput and system resource utilizations. | ||||||||
| AMPLab | Compares performance of five popular SQL query engines in the Big Data ecosystem on common types of queries, which can be reproduced through publicly available scripts and datasets. | ||||||||
