Data Ingestion

Transferring real-time data stream processed by Apache Flink to Kafka to Druid for analysis

Businesses can react quickly and effectively to user behavior patterns by using real-time analytics. This allows them to take advantage of opportunities that might otherwise pass them by and prevent problems from getting worse. Apache Kafka, a popular event streaming platform, can be used for real-time ingestion of data/events generated from various sources across multiple verticals such as IoT, financial transactions, inventory, etc. This data can then be streamed into multiple downstream applications or engines for further processing and eventual...

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Druid Kafka Supervisor

Understanding Apache Druid Supervisor and its specification for real-time data ingestion from Apache Kafka

Although both Apache Druid and Apache Kafka are potent open-source data processing tools, they have diverse uses. While Druid is a high-performance, column-store, real-time analytical database, Kafka is a distributed platform for event streaming. However, they can work together in a typical data pipeline scenario where Kafka is used as a messaging system to ingest and store data/events, and Druid is used to perform real-time analytics on that data. In short, the indexing is the process of loading data in Druid...

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Why Kappa Architecture for processing of streaming data. Have competence to superseding Lambda Architecture?

Data is quickly becoming the new currency of the digital economy, but it is useless if it can’t be processed. The processing of data is essential for subsequent decision-making or executable actions either by the human brain or various devices/applications etc. There are two primary ways of processing data namely batch processing and stream processing. Typically batch processing has been adopted for very large data sets and projects where there is a necessity for deeper data analysis, on the...

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iDropper – The Data Ingestion, Monitoring and Reporting Tool

In today’s complicated world of business, the data, organizations own and how they use it, make them different from others to innovate, to compete better and to stay ahead in the business. That’s the driving factor for the organizations to collect and process as much data as possible, transform it into information with data-driven discoveries, and deliver it to the end user in the right format for smart decision-making. Common Challenges/Concerns Fetching the raw data files from the various data...

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Steering number of mapper (MapReduce) in sqoop for parallelism of data ingestion into Hadoop Distributed File System (HDFS)

To import data from most the data source like RDBMS, sqoop internally use mapper. Before delegating the responsibility to the mapper, sqoop performs few initial operations in a sequence once we execute the command on a terminal in any node in the Hadoop cluster. Ideally, in production environment, sqoop installed in the separate node and updated .bashrc file to append sqoop's binary and configuration which helps to execute sqoop command from anywhere in the multi-node cluster. Most of the...

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Data Ingestion phase for migrating enterprise data into Hadoop Data Lake

The Big Data solutions helps to achieve valuable information to iron out the accurate strategic business decision. Exponential growth of digitalization, social media, telecommunication etc. are fueling enormous data generation everywhere. Prior to process of huge volume of data, we should have efficient data storage mechanism in a distributed manner to hold any form of data starting from structured to unstructured. Hadoop distributed file systems (HDFS) can be leveraged efficiently as data lake by installing on multi node cluster....

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Apache Kafka, The next Generation Distributed Messaging System

In Big Data project, the main challenge is to collect an enormous volume of data. We need distributed high throughput messaging systems to overcome it. Apache Kafka is designed to address the challenge. It was originally developed at LinkedIn Corporation and later on became a part of Apache project. A Messaging System is typically responsible for transferring data from one application to another. A message is nothing but the bunch of data/information. To ingest huge volume of data into Hadoop...

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Establishment of Data Lake specific to multi-channel e-commerce application to understand customer’s buying pattern

Post order fulfillment data is becoming a very important asset of e-commerce vendors to understand complete buying pattern of customers. Especially for the e-commerce vendors who sells multiple products starting from electronics to apparels. Extraction and transformation are time-consuming operations when partially structured data starts moving from the various sources and finally land into the relational data warehouse.  Data extracted from the social media are semi-structured (JSON or XML).  As an example, Facebook provides information in JSON format through Graph API and same...

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Ingesting Big Data into HDFS

we are always talking about Big data processing using Hadoop. And know the basic definition of Big Data which is huge volume of data those can not be stored in existing traditional database or data repository. Interestingly, how can we import such a huge volume of data to the cluster of computers where Hadoop is installed? Yes, using Flume we can continuously collect the stream of data. For example Twitter data can be collected for analysis of comments. Sqoop...

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