Wiley  Mayer

Wiley Mayer

1621916997

Understanding the Lag in Your Kafka Cluster

Kafka powers compelling consumer experiences in the companies. Consumer lag is a big challenge in Kafka. Understand and address consumer lag in Kafka.

Amongst various metrics that Kafka monitoring includes consumer lag is nearly the most important of them all. In this post, we will explore potential reasons for Kafka consumer lag and what you could do when you experience lag.

Kafka — Past and Present

Apache Kafka is no longer used just by the Internet hyperscalers. Apache Kafka is used in the enterprise to deal with exploding streaming data. It powers compelling consumer experiences such as real-time personalization, recommendation and next best action. Kafka allows low latency ingestion of large amounts of data into data lakes or data warehouses. Kafka allows businesses to get real-time intelligence into their business operations that allows them to react in real time to changing business conditions.

Mission-critical business processes are plagued by consumer lags, and experienced practitioners agree that preventing consumer lag is the biggest challenge in Kafka.

#kafka #big-data

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Understanding the Lag in Your Kafka Cluster
Ruth  Nabimanya

Ruth Nabimanya

1621289940

Tips About Kafka Connect On Heroku You Can't Afford To Miss

Introduction

With ever-increasing demands from other business units, IT departments have to be constantly looking for service improvements and cost-saving opportunities. This article showcases several concrete use-cases for companies that are investigating or already using Kafka, in particular, Kafka Connect.

Kafka Connect is an enterprise-grade solution for integrating a plethora of applications, ranging from traditional databases to business applications like Salesforce and SAP. Possible integration scenarios range from continuously streaming events and data between applications to large-scale, configurable batch jobs that can be used to replace manual data transfers.

#kafka-connect #kafka #heroku #database #database-architecture #apache-kafka #tutorial #cluster

akshay L

akshay L

1572344038

Kafka Spark Streaming | Kafka Tutorial

In this kafka spark streaming tutorial you will learn what is apache kafka, architecture of apache kafka & how to setup a kafka cluster, what is spark & it’s features, components of spark and hands on demo on integrating spark streaming with apache kafka and integrating spark flume with apache kafka.

# Kafka Spark Streaming #Kafka Tutorial #Kafka Training #Kafka Course #Intellipaat

Wiley  Mayer

Wiley Mayer

1621916997

Understanding the Lag in Your Kafka Cluster

Kafka powers compelling consumer experiences in the companies. Consumer lag is a big challenge in Kafka. Understand and address consumer lag in Kafka.

Amongst various metrics that Kafka monitoring includes consumer lag is nearly the most important of them all. In this post, we will explore potential reasons for Kafka consumer lag and what you could do when you experience lag.

Kafka — Past and Present

Apache Kafka is no longer used just by the Internet hyperscalers. Apache Kafka is used in the enterprise to deal with exploding streaming data. It powers compelling consumer experiences such as real-time personalization, recommendation and next best action. Kafka allows low latency ingestion of large amounts of data into data lakes or data warehouses. Kafka allows businesses to get real-time intelligence into their business operations that allows them to react in real time to changing business conditions.

Mission-critical business processes are plagued by consumer lags, and experienced practitioners agree that preventing consumer lag is the biggest challenge in Kafka.

#kafka #big-data

Elton  Bogan

Elton Bogan

1600190040

SciPy Cluster - K-Means Clustering and Hierarchical Clustering

SciPy is the most efficient open-source library in python. The main purpose is to compute mathematical and scientific problems. There are many sub-packages in SciPy which further increases its functionality. This is a very important package for data interpretation. We can segregate clusters from the data set. We can perform clustering using a single or multi-cluster. Initially, we generate the data set. Then we perform clustering on the data set. Let us learn more SciPy Clusters.

K-means Clustering

It is a method that can employ to determine clusters and their center. We can use this process on the raw data set. We can define a cluster when the points inside the cluster have the minimum distance when we compare it to points outside the cluster. The k-means method operates in two steps, given an initial set of k-centers,

  • We define the cluster data points for the given cluster center. The points are such that they are closer to the cluster center than any other center.
  • We then calculate the mean for all the data points. The mean value then becomes the new cluster center.

The process iterates until the center value becomes constant. We then fix and assign the center value. The implementation of this process is very accurate using the SciPy library.

#numpy tutorials #clustering in scipy #k-means clustering in scipy #scipy clusters #numpy

Diving Deep into Kafka

The objective of this blog is to build some more understanding of Apache Kafka concepts such as Topics, Partitions, Consumer, and Consumer Groups. Kafka’s basic concepts have been covered in my previous article.

Kafka Topic & Partitions

As we know, messages in Kafka are categorized or stored inside Topics. In simple terms, Topic can be construed as a Database table. Kafka Topics inside is broken down into partitions. Partitions allow us to parallelize a topic by splitting the data of a topic across multiple brokers, thus adding an essence of parallelism to the ecosystem.

Behind the scenes

Messages are written to a partition in an append-only manner, and messages are read from a partition from beginning to end, FIFO mannerism. Each message within a partition is identified by an integer value called _offset. _An offset is an immutable sequential ordering of messages, maintained by Kafka. Anatomy of a Topic with multiple partitions:

Image for post

Partitioned Topic

Sequential number in array fashion is the offset value maintained by Kafka

Some key points:

  1. Ordering of messages is maintained at the partition level, not across the topic.
  2. Data written to partition is immutable and can’t be updated.
  3. Each message in the Kafka broker is a collection of message topics, partition, offset, key, and value.
  4. Each partition will have a leader that will take care of Read/Write operations in the partition.

#kafka-python #kafka #streaming #apache-kafka