Ian  Robinson

Ian Robinson

1624434540

Everything You Need to Know about Apache Storm

The ever-increasing growth in the production and analytics of Big Data keeps presenting new challenges, and the data scientists and programmers gracefully take it in their stride – by constantly improving the applications developed by them. One such problem was that of real-time streaming. Real-time data holds extremely high value for businesses, but it has a time-window after which it loses its value – an expiry date, if you will. If the value of this real-time data is not realised within the window, no usable information can be extracted from it. This real-time data comes in quickly and continuously, therefore the term “Streaming”.

Analytics of this real-time data can help you stay updated on what’s happening right now, such as the number of people reading your blog post, or the number of people visiting your Facebook page. Although it might sound like just a “nice-to-have” feature, in practice, It is essential. Imagine you’re a part of an Ad Agency performing real-time analytics on your ad-campaigns – that the client paid heavily for. Real-time analytics can keep you posted on how is your Ad performing in the market, how the users are responding to it, and other things of that nature. Quite an essential tool if you think of it this way, right?

Looking at the value that real-time data holds, organisations started coming up with various real-time data analytics tools. In this article, we’ll be talking about one of those – Apache Storm. We’ll look at what it is, the architecture of a typical storm application, it’s core components (also known as abstractions), and its real life-use cases.

Let’s go!

Table of Contents

#big data #data #technical skills #technology #everything you need to know about apache storm #apache storm

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Everything You Need to Know about Apache Storm
Ian  Robinson

Ian Robinson

1624434540

Everything You Need to Know about Apache Storm

The ever-increasing growth in the production and analytics of Big Data keeps presenting new challenges, and the data scientists and programmers gracefully take it in their stride – by constantly improving the applications developed by them. One such problem was that of real-time streaming. Real-time data holds extremely high value for businesses, but it has a time-window after which it loses its value – an expiry date, if you will. If the value of this real-time data is not realised within the window, no usable information can be extracted from it. This real-time data comes in quickly and continuously, therefore the term “Streaming”.

Analytics of this real-time data can help you stay updated on what’s happening right now, such as the number of people reading your blog post, or the number of people visiting your Facebook page. Although it might sound like just a “nice-to-have” feature, in practice, It is essential. Imagine you’re a part of an Ad Agency performing real-time analytics on your ad-campaigns – that the client paid heavily for. Real-time analytics can keep you posted on how is your Ad performing in the market, how the users are responding to it, and other things of that nature. Quite an essential tool if you think of it this way, right?

Looking at the value that real-time data holds, organisations started coming up with various real-time data analytics tools. In this article, we’ll be talking about one of those – Apache Storm. We’ll look at what it is, the architecture of a typical storm application, it’s core components (also known as abstractions), and its real life-use cases.

Let’s go!

Table of Contents

#big data #data #technical skills #technology #everything you need to know about apache storm #apache storm

Apache Storm Security With Kerberos

Apache Storm Security plays an important role to manage the smooth functioning of the operational database. Before starting with how we can secure Storm with the help of Kerberos let us discuss what actually is Storm and Kerberos are:

Storm or we called it as apache storm is a distributed real-time computation system which is free and open source. As the number of IOT devices is increasing at an enormous range which results in high streams of data at a very short interval which results that we need very large data memories for storing, processing and analyzing these heavy data to get some actionable results. In this case, apache storm is the best method to deal with this situation. Apache storm helps in storing, processing, analyzing and publishing real-time data without storing any actual data.

#insights #apache #apache storm

Obie  Rowe

Obie Rowe

1605599980

Everything You Need to Know About Kafka

What is Apache Kafka®?

What is event streaming?

Event streaming is the digital equivalent of the human body’s central nervous system. It is the technological foundation for the ‘always-on’ world where businesses are increasingly software-defined and automated, and where the user of software is more software.

Technically speaking, event streaming is the practice of capturing data in real-time from event sources like databases, sensors, mobile devices, cloud services, and software applications in the form of streams of events; storing these event streams durably for later retrieval; manipulating, processing, and reacting to the event streams in real-time as well as retrospectively; and routing the event streams to different destination technologies as needed. Event streaming thus ensures a continuous flow and interpretation of data so that the right information is at the right place, at the right time.

What can I use event streaming for?

Event streaming is applied to a wide variety of use cases across a plethora of industries and organizations. Its many examples include:

  • To process payments and financial transactions in real-time, such as in stock exchanges, banks, and insurances.
  • To track and monitor cars, trucks, fleets, and shipments in real-time, such as in logistics and the automotive industry.
  • To continuously capture and analyze sensor data from IoT devices or other equipment, such as in factories and wind parks.
  • To collect and immediately react to customer interactions and orders, such as in retail, the hotel and travel industry, and mobile applications.
  • To monitor patients in hospital care and predict changes in condition to ensure timely treatment in emergencies.
  • To connect, store, and make available data produced by different divisions of a company.
  • To serve as the foundation for data platforms, event-driven architectures, and microservices.
Apache Kafka® is an event streaming platform. What does that mean?

Kafka combines three key capabilities so you can implement your use cases for event streaming end-to-end with a single battle-tested solution:

  1. To publish (write) and subscribe to (read) streams of events, including continuous import/export of your data from other systems.
  2. To store streams of events durably and reliably for as long as you want.
  3. To process streams of events as they occur or retrospectively.

And all this functionality is provided in a distributed, highly scalable, elastic, fault-tolerant, and secure manner. Kafka can be deployed on bare-metal hardware, virtual machines, and containers, and on-premises as well as in the cloud. You can choose between self-managing your Kafka environments and using fully managed services offered by a variety of vendors.

How does Kafka work in a nutshell?

Kafka is a distributed system consisting of servers and clients that communicate via a high-performance TCP network protocol. It can be deployed on bare-metal hardware, virtual machines, and containers in on-premise as well as cloud environments.

Servers: Kafka is run as a cluster of one or more servers that can span multiple datacenters or cloud regions. Some of these servers form the storage layer, called the brokers. Other servers run Kafka Connect to continuously import and export data as event streams to integrate Kafka with your existing systems such as relational databases as well as other Kafka clusters. To let you implement mission-critical use cases, a Kafka cluster is highly scalable and fault-tolerant: if any of its servers fails, the other servers will take over their work to ensure continuous operations without any data loss.

Clients: They allow you to write distributed applications and microservices that read, write, and process streams of events in parallel, at scale, and in a fault-tolerant manner even in the case of network problems or machine failures. Kafka ships with some such clients included, which are augmented by dozens of clients provided by the Kafka community: clients are available for Java and Scala including the higher-level Kafka Streams library, for Go, Python, C/C++, and many other programming languages as well as REST APIs.

Main Concepts and Terminology

An event records the fact that “something happened” in the world or in your business. It is also called record or message in the documentation. When you read or write data to Kafka, you do this in the form of events. Conceptually, an event has a key, value, timestamp, and optional metadata headers. Here’s an example event:

  • Event key: “Alice”
  • Event value: “Made a payment of $200 to Bob”
  • Event timestamp: “Jun. 25, 2020 at 2:06 p.m.”

Producers are those client applications that publish (write) events to Kafka, and consumers are those that subscribe to (read and process) these events. In Kafka, producers and consumers are fully decoupled and agnostic of each other, which is a key design element to achieve the high scalability that Kafka is known for. For example, producers never need to wait for consumers. Kafka provides various guarantees such as the ability to process events exactly-once.

Events are organized and durably stored in topics. Very simplified, a topic is similar to a folder in a filesystem, and the events are the files in that folder. An example topic name could be “payments”. Topics in Kafka are always multi-producer and multi-subscriber: a topic can have zero, one, or many producers that write events to it, as well as zero, one, or many consumers that subscribe to these events. Events in a topic can be read as often as needed—unlike traditional messaging systems, events are not deleted after consumption. Instead, you define for how long Kafka should retain your events through a per-topic configuration setting, after which old events will be discarded. Kafka’s performance is effectively constant with respect to data size, so storing data for a long time is perfectly fine.

Topics are partitioned, meaning a topic is spread over a number of “buckets” located on different Kafka brokers. This distributed placement of your data is very important for scalability because it allows client applications to both read and write the data from/to many brokers at the same time. When a new event is published to a topic, it is actually appended to one of the topic’s partitions. Events with the same event key (e.g., a customer or vehicle ID) are written to the same partition, and Kafka guarantees that any consumer of a given topic-partition will always read that partition’s events in exactly the same order as they were written.

Figure: This example topic has four partitions P1–P4. Two different producer clients are publishing, independently from each other, new events to the topic by writing events over the network to the topic’s partitions. Events with the same key (denoted by their color in the figure) are written to the same partition. Note that both producers can write to the same partition if appropriate.

To make your data fault-tolerant and highly-available, every topic can be replicated, even across geo-regions or datacenters, so that there are always multiple brokers that have a copy of the data just in case things go wrong, you want to do maintenance on the brokers, and so on. A common production setting is a replication factor of 3, i.e., there will always be three copies of your data. This replication is performed at the level of topic-partitions.

This primer should be sufficient for an introduction. The Design section of the documentation explains Kafka’s various concepts in full detail, if you are interested.

Kafka APIs

In addition to command line tooling for management and administration tasks, Kafka has five core APIs for Java and Scala:

  • The Admin API to manage and inspect topics, brokers, and other Kafka objects.
  • The Producer API to publish (write) a stream of events to one or more Kafka topics.
  • The Consumer API to subscribe to (read) one or more topics and to process the stream of events produced to them.
  • The Kafka Streams API to implement stream processing applications and microservices. It provides higher-level functions to process event streams, including transformations, stateful operations like aggregations and joins, windowing, processing based on event-time, and more. Input is read from one or more topics in order to generate output to one or more topics, effectively transforming the input streams to output streams.
  • The Kafka Connect API to build and run reusable data import/export connectors that consume (read) or produce (write) streams of events from and to external systems and applications so they can integrate with Kafka. For example, a connector to a relational database like PostgreSQL might capture every change to a set of tables. However, in practice, you typically don’t need to implement your own connectors because the Kafka community already provides hundreds of ready-to-use connectors.
Where to go from here

Originally published at https://kafka.apache.org

#kafka #apache #apache-kafka #big-data #developer

David mr

David mr

1624305600

SAFEMOON UPDATE - ALL YOU NEED TO KNOW ABOUT SAFEMOON AND SAFEMOON PREDICTION

SAFEMOON UPDATE - ALL YOU NEED TO KNOW ABOUT SAFEMOON AND SAFEMOON PREDICTION

This is all you need to know about safemoon and I provide my safemoon prediction. This is a huge safemoon update so make sure to watch this video until the end.
📺 The video in this post was made by Josh’s Finance
The origin of the article: https://www.youtube.com/watch?v=ZtX7ZIVcXH4
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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#bitcoin #blockchain #safemoon #safemoon #all you need to know about safemoon and safemoon prediction #safemoon update - all you need to know about safemoon and safemoon prediction

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