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The Hadoop Common package contains the Java Archive (JAR) files and scripts needed to start Hadoop. For effective scheduling of work, every Hadoop-compatible file system should provide location awareness, which is the name of the rack, specifically the network switch where a worker node is.
Early data lakes, such as Hadoop 1.0, had limited capabilities because it only supported batch-oriented processing . Interacting with it required expertise in Java, map reduce and higher-level tools like Apache Pig, Apache Spark and Apache Hive (which were also originally batch-oriented).
[clarification needed] This is also why sharding is related to a shared-nothing architecture—once sharded, each shard can live in a totally separate logical schema instance / physical database server / data center / continent. There is no ongoing need to retain shared access (from between shards) to the other unpartitioned tables in other shards.
Apache HBase began as a project by the company Powerset out of a need to process massive amounts of data for the purposes of natural-language search. Since 2010 it is a top-level Apache project. Facebook elected to implement its new messaging platform using HBase in November 2010, but migrated away from HBase in 2018. [4]
Hive provides the necessary SQL abstraction to integrate SQL-like queries into the underlying Java without the need to implement queries in the low-level Java API. Hive facilitates the integration of SQL-based querying languages with Hadoop, which is commonly used in data warehousing applications. [5]
Twill: Use Apache Hadoop YARN's distributed capabilities with a programming model that is similar to running threads Usergrid : an open-source Backend-as-a-Service ("BaaS" or "mBaaS") composed of an integrated distributed NoSQL database, application layer and client tier with SDKs for developers looking to rapidly build web and/or mobile ...
Avro is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.
Cascading is a software abstraction layer for Apache Hadoop and Apache Flink. Cascading is used to create and execute complex data processing workflows on a Hadoop cluster using any JVM-based language (Java, JRuby, Clojure, etc.), hiding the underlying complexity of MapReduce jobs. It is open source and available under the Apache License.