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Apache NiFi is a software project from the Apache Software Foundation designed to automate the flow of data between software systems.Leveraging the concept of extract, transform, load (ETL), it is based on the "NiagaraFiles" software previously developed by the US National Security Agency (NSA), which is also the source of a part of its present name – NiFi.
HBase: Apache HBase software is the Hadoop database. Think of it as a distributed, scalable, big data store; Helix: a cluster management framework for partitioned and replicated distributed resources; Hive: the Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.
A common source of problems in ETL is a big number of dependencies among ETL jobs. For example, job "B" cannot start while job "A" is not finished. One can usually achieve better performance by visualizing all processes on a graph, and trying to reduce the graph making maximum use of parallelism , and making "chains" of consecutive processing ...
If so, what is its advantage over just piping output between tools? Also, can NiFi be used in the situation where there's a constant stream of incoming data, as opposed to a finite file of data to be processed? Enquiring minds want to know! After I posted the above, I ran across this page: NiFi Tutorial. It answers some of my questions, but ...
The Apache HTTP Server (/ ə ˈ p æ tʃ i / ə-PATCH-ee) is a free and open-source cross-platform web server, released under the terms of Apache License 2.0. It is developed and maintained by a community of developers under the auspices of the Apache Software Foundation .
Example of a database that can be used by a data lake (in this case structured data) A data lake is a system or repository of data stored in its natural/raw format, [1] usually object blobs or files.
The Apache Thrift API client/server architecture. Thrift includes a complete stack for creating clients and servers. [9] The top part is generated code from the Thrift definition. From this file, the services generate client and processor codes. In contrast to built-in types, created data structures are sent as a result of generated code.
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