Incorporating NiFi Into Brownfield Code

Kevin Feasel

2016-06-07

ETL, Hadoop

Paul Boal discusses how he incorporated Apache NiFi in an existing process:

Typically, data warehousing and ETL tool vendors recommended that we write your own custom components. After all, the target market for ETL tools is a space where the tools are specifically marketed as reducing the need for “error prone and time consuming” manual coding. When I ran across this tutorial on writing your own NiFi processor it occurred to me that NiFi is the exact opposite. It’s both Open Source and designed for extensibility from the ground up. I found it quite reasonable to write a custom NiFi processor that leverages our existing code base.

The existing code is a Java program with separate classes for each device vendor, all with the same interface to abstract the nuances of each vendor from the main data export program. This interface follows a traditional paradigm: login, query, query, query, logout. Given that my input to NiFi above takes in simple username, password, and query criteria arguments, it seems trivial to create a NiFi processor class that adapts the existing code into the NiFi API. Here’s a slightly abbreviated version of the actual code. (In reality, it’s all of 70 lines of code.)

In almost any realistic scenario, you’re not going to have the opportunity to start from scratch.  You will always have legacy components, external dependencies, and existing user bases to satisfy.  I like this article because it moves forward from that starting point.

Related Posts

Testing Kafka Streams Applications

Yeva Byzek continues her series on testing Kafka-based streaming applications: When you create a stream processing application with Kafka’s Streams API, you create a Topologyeither using the StreamsBuilder DSL or the low-level Processor API. Normally, the topology runs with the KafkaStreams class, which connects to a Kafka cluster and begins processing when you call start(). For testing though, connecting to a running […]

Read More

Auto ML With SQL Server 2019 Big Data Clusters

Marco Inchiosa has a model scenario for using Big Data Clusters to scale out a machine learning problem: H2O provides popular open source software for data science and machine learning on big data, including Apache SparkTM integration. It provides two open source python AutoML classes: h2o.automl.H2OAutoML and pysparkling.ml.H2OAutoML. Both APIs use the same underlying algorithm implementations, […]

Read More

Categories

June 2016
MTWTFSS
« May Jul »
 12345
6789101112
13141516171819
20212223242526
27282930