Datascience: Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
Machine Learning:
Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead.
Deep Learning:
Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms.
Hadoop:
Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. I
MapReduce:
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.
Hive:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis.
Pig:
Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin.
Impala:
Apache Impala is an open source massively parallel processing SQL query engine for data stored in a computer cluster running Apache Hadoop. Impala has been described as the open-source equivalent of Google F1, which inspired its development in 2012.
Spark:
Apache Spark is an open-source distributed general-purpose cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since.
Spark streaming:
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. ... Finally, processed data can be pushed out to filesystems, databases, and live dashboards.
Flume:
Apache Flume is a distributed, reliable, and available software for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows.
Flink:
Apache Flink is an open source stream processing framework developed by the Apache Software Foundation. The core of Apache Flink is a distributed streaming dataflow engine written in Java and Scala. Flink executes arbitrary dataflow programs in a data-parallel and pipelined manner.
Hbase:
HBase is an open-source, non-relational, distributed database modeled after Google's Bigtable and written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS or Alluxio, providing Bigtable-like capabilities for Hadoop.
Mongodb:
MongoDB is a cross-platform document-oriented database program. Classified as a NoSQL .... The language drivers are available under an Apache License.
Cassandra:
Apache Cassandra is a free and open-source, distributed, wide column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure.
RainMeter:
Rainmeter is a free and open-source desktop customization utility for Windows, released under the GNU GPL v2 license. It allows users to create and display user-generated customizable desktop widgets or applets called "skins" that display information.
Tableau:
Tableau Software is a software company headquartered in Seattle, Washington, United States that produces interactive data visualization products focused on business intelligence.
Qlik-View:
QlikView is a Business Intelligence (BI) data discovery product for creating guided analytics applications and dashboards tailor-made for business challenges. ... This tool exposes data that is not found with query-based tools.
kafka:
Apache Kafka is an open-source stream-processing software platform developed by LinkedIn and donated to the Apache Software Foundation, written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
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