Gilberto  Block

Gilberto Block

1595063428

Running Streaming ETL Pipelines with Apache Flink on Zeppelin Notebooks

A step-by-step tutorial for running Streaming ETL with Flink on Zeppelin. Let’s dive deeper into the Flink interpreter in Zeppelin Notebooks.

Apache Zeppelin 0.9 comes with a redesigned interpreter for Apache Flink that allows developers and data engineers to use Flink directly on Zeppelin notebooks for interactive data analysis. Over the next paragraphs, we describe why Streaming ETL is a great fit for stream processing frameworks like Apache Flink and we dive deeper into the Flink interpreter in Zeppelin Notebooks by showcasing a tutorial of how developers can run Streaming ETL data pipelines with Flink on Zeppelin.

zeppelin image

Streaming ETL and Apache Flink

Extract-transform-load (ETL) is a common operation related to massaging and moving data between storage systems. ETL jobs have historically been triggered periodically, frequently copying data from transactional database systems to an analytical database or a data warehouse.

Streaming ETL pipelines serve a similar purpose traditional ETL: they transform and enrich data and can move it from one storage system to another. However, streaming ETL pipelines are different from traditional ETL in that they operate continuously and are capable of both reading records from sources that continuously produce data as well as moving the data, with low latency, to their desired destination.

Streaming ETL is a common use case for Apache Flink because of its ability to address most common data transformation or enrichment tasks with Flink SQL (or Table API) and its support for user-defined functions. Additionally, Flink provides a rich set of connectors to various storage systems such as KafkaKinesisElasticsearch, and JDBC database systems. It also features continuous sources for file systems that monitor directories and sinks and write files in a time-bucketed fashion. Let us now describe how the Flink interpreter works in Zeppelin notebooks.

#open source #tutorial #apache flink #apache zeppelin #apache

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Running Streaming ETL Pipelines with Apache Flink on Zeppelin Notebooks
Gerhard  Brink

Gerhard Brink

1622108520

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics

Gerhard  Brink

Gerhard Brink

1620722340

Flink: Join two Data Streams

Reading Time: 3 minutes

Apache Flink offers rich sources of API and operators which makes Flink application developers productive in terms of dealing with the** multiple data streams**. Flink provides many multi streams operations like UnionJoin, and so on. In this blog, we will explore the Window Join operator in Flink with an example. It joins two data streams on a given key and a common window.

Let say we have one stream which contains salary information of all the individual who belongs to an organization. The salary information has the id, name, and salary of an individual. This stream is available at port 9000 on the localhost.

#apache flink #big data and fast data #flink #java ##apache flink #big #big data analytics #fast data analytics #flink streaming #joins #streaming #streaming analytics

Gilberto  Block

Gilberto Block

1595063428

Running Streaming ETL Pipelines with Apache Flink on Zeppelin Notebooks

A step-by-step tutorial for running Streaming ETL with Flink on Zeppelin. Let’s dive deeper into the Flink interpreter in Zeppelin Notebooks.

Apache Zeppelin 0.9 comes with a redesigned interpreter for Apache Flink that allows developers and data engineers to use Flink directly on Zeppelin notebooks for interactive data analysis. Over the next paragraphs, we describe why Streaming ETL is a great fit for stream processing frameworks like Apache Flink and we dive deeper into the Flink interpreter in Zeppelin Notebooks by showcasing a tutorial of how developers can run Streaming ETL data pipelines with Flink on Zeppelin.

zeppelin image

Streaming ETL and Apache Flink

Extract-transform-load (ETL) is a common operation related to massaging and moving data between storage systems. ETL jobs have historically been triggered periodically, frequently copying data from transactional database systems to an analytical database or a data warehouse.

Streaming ETL pipelines serve a similar purpose traditional ETL: they transform and enrich data and can move it from one storage system to another. However, streaming ETL pipelines are different from traditional ETL in that they operate continuously and are capable of both reading records from sources that continuously produce data as well as moving the data, with low latency, to their desired destination.

Streaming ETL is a common use case for Apache Flink because of its ability to address most common data transformation or enrichment tasks with Flink SQL (or Table API) and its support for user-defined functions. Additionally, Flink provides a rich set of connectors to various storage systems such as KafkaKinesisElasticsearch, and JDBC database systems. It also features continuous sources for file systems that monitor directories and sinks and write files in a time-bucketed fashion. Let us now describe how the Flink interpreter works in Zeppelin notebooks.

#open source #tutorial #apache flink #apache zeppelin #apache

Myrl  Prosacco

Myrl Prosacco

1594533600

Using Apache Flink for Kinesis to Kafka Connect

In this blog, we are going to use kinesis as a source and kafka as a consumer.

Let’s get started.

Step 1:

Apache Flink provides the kinesis and kafka connector dependencies. Let’s add them in our build.sbt:

name := "flink-demo"

version := "0.1"

scalaVersion := "2.12.8"

libraryDependencies ++= Seq(
  "org.apache.flink" %% "flink-scala" % "1.10.0",
  "org.apache.flink" %% "flink-connector-kinesis" % "1.10.0",
  "org.apache.flink" %% "flink-connector-kafka" % "1.10.0",
  "org.apache.flink" %% "flink-streaming-scala" % "1.10.0"
)

Step 2:

The next step is to create a pointer to the environment on which this program runs.

val env = StreamExecutionEnvironment.getExecutionEnvironment

Step 3:

Setting parallelism of x here will cause all operators (such as join, map, reduce) to run with x parallel instance.

I am using 1 as it is a demo application.

env.setParallelism(1)

Step 4:

Disabling the aws cbor, as we are testing locally.

System.setProperty("com.amazonaws.sdk.disableCbor", "true")
System.setProperty("org.apache.flink.kinesis.shaded.com.amazonaws.sdk.disableCbor", "true")

Step 5:

Defining Kinesis consumer properties.

  • Region
  • Stream Position – TRIM_HORIZON to read all the records available in the stream
  • Aws keys
  • Do not worry about the endpoint, it is set to http://localhost:4568 as we will test the kinesis using localstack.

Do not worry about the endpoint, it is set to http://localhost:4568 as we will test the kinesis using localstack.

#apache flink #flink #scala ##apache-flink ##kinesis #apache #flink streaming #kafka #scala

Dedric  Reinger

Dedric Reinger

1599116040

Basic Anatomy of a Flink Program

Hi Folks! Hope you all are safe in the COVID-19 pandemic and learning new tools and tech while staying at home. I also have just started learning a very prominent Big Data** framework** for stream processing which is  Flink. Flink is a distributed framework and based on the streaming first principle, means it is a real streaming processing engine and implements batch processing as a special case. In this blog, we will see the basic anatomy of a Flink program. So, this blog will help us to understand the basic structure of a Flink program and how we can start writing a basic Flink Application.

Let’s explore the steps involves in setting up the streaming application in Flink with a simple example. In the example, we will read messages in the form of text from the socket text stream. Then filter out the streaming text if it is a number. The Flink application for this use case will be accomplished in 5 steps as shown below.

Step 1: Setup Execution Environment

The very first step is to let Flink knows the right environment for application means whether the streaming application is going to be run locally or on some machines need to connect. So, we need to create a stream execution environment.

StreamExecutionEnvironment executionEnvironment =
       StreamExecutionEnvironment.getExecutionEnvironment();

#apache flink #big data and fast data #flink #java ##apache flink ##flink #big data #big data analytics #fast data #stream processing #streaming