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25AugAugust 25, 2022

Importance of Schema Registry on Kafka Based Data Streaming Pipelines

Needless to say Apache Kafka delivers messages to both real-time and batch consumers without performance degradation and in addition to that gaining enormous momentum as a foremost component for data streaming pipelines too. Credit card fraud detection, predictive maintenance, or real-time analytics, building streaming IoT platform, etc are the example of real-time use cases. To handle massive amounts of data ingestion, Apache Kafka is the cornerstone of a robust IoT data platform. A schema defines the structure of the data...

By Gautam GoswamiApache Kafka, Architecture, Data Engineering, Data IngestionApache Kafka, assign schema info in the schema registry, Avro, building streaming IoT platform, centralized schema management, Confluent Schema Registry, Credit card fraud detection, Data ingestion, data pipeline, Data Pipelines, Data Streaming, deserialized the messages, distributed storage layer for schemas, Hadoop, JSON Schema, Kafka based data pipeline, Kafka based data streaming pipelines, Kafka connect, Kafka producers and consumers, multi-broker Kafka topic, Multi-node kafka cluster, predictive maintenance, producer-consumer contract, Protobuf schemas, real-time analytics, schema change history, schema evolution, schema of registered data streams, schema registry, Schema Registry on Kafka Based Data Streaming Pipelines, Schema Registry on Kafka Streaming Pipelines, service layer for metadata, streaming applications, streaming data to Kafka topicComments Off

11JanJanuary 11, 2021

Data Absorption (Ingestion)

By Kislay KomalAbsorption, accumulate business data, Apache Flume, Apache Kafka, Apache Sqoop, Big Data Analysis service in Bangalore, Big data ingestion architecture, central data repository, creating hadoop data lake, Data Absorption, Data Absorption Service in Bangalore, data analytics, Data ingestion, data ingestion architecture, Data Ingestion Service in Bangalore, Data Lake, data pipeline, distributed centralized repository, Hadoop Data Lake, Hadoop Data Lake Creation, heterogeneous data, how to create hadoop data lake, IDropper, Ingestion, ingestion architecture, Kafka, moving heterogeneous data, poc, preserve business data, production server log files, proof of concept, rdbms, Sqoop, structured data, unstructured data, using apache flumeComments Off

11JanJanuary 11, 2021

Distributed Incubator

By Kislay KomalApache Hadoop, AWS Cloud, Big Data Analysis service in Bangalore, construct the Data Lake, customer inclination, customer sentiments, Data Absorption Service in Bangalore, Data ingestion, Data Ingestion Service in Bangalore, Data Lake, Data Transformation, data visibility, Distributed Data Incubator, distributed data storage, Distributed Incubator, ELT concept, extracting heterogeneous data, fault tolerance, Hadoop Data Lake, Hadoop Data Lake Creation, Hadoop Distributed File System, persisting data, replication, traditional databases, unbounded storage capacityComments Off

17SepSeptember 17, 2017

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...

By Gautam GoswamiData Engineering, Data IngestionData ingestion, Hadoop Distributed File System, HDFS, Map Reduce, parallelism of data ingestion, Sqoop, sqoop for parallelism of data ingestion into Hadoop Distributed File System (HDFS)Comments Off

08SepSeptember 8, 2017

Transfer structured data from Oracle to Hadoop storage system

Using Apache's sqoop, we can transfer structured data from Relational Database Management System to Hadoop distributed file system (HDFS). Because of distributed storage mechanism in Hadoop Distributed File System (HDFS), we can store any format of data in huge volume in terms of capacity. In RDBMS, data persists in the row and column format (Known as Structured Data). In order to process the huge volume of enterprise data, we can leverage HDFS as a basic data lake. In this...

By Gautam GoswamiData Engineering, Hadoop Eco SystemAmazon web service, Apache Sqoop, Apache's sqoop, Data ingestion, Data ingestion mechanism, distributed storage, distributed storage mechanism, enterprise data, Google cloud, Hadoop, Hadoop 2.x, Hadoop Distributed File System, Hadoop storage system, HDFS, huge volume of enterprise data, Microsoft Azure, multi node cluster, Oracle to Hadoop, Sqoop, structured data, Transfer structured data from Oracle to Hadoop, Using Apache sqoopComments Off

29AugAugust 29, 2017

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....

By Gautam GoswamiData Engineering, Data IngestionApache software foundation, Apache Sqoop, ata storage mechanism, ATG database, ATG database schema, cloud service providers, collecting Twitter streaming data, Couchbase, Data ingestion, Data Ingestion phase for migrating enterprise data into Hadoop Data Lake, Data Lake, data storage mechanism, DB2, Digitization, distributed storage, efficient data storage mechanism, ELT, enterprise data, export data from Kafka topic to HDFS, fault-tolerant, Flume, Hadoop, HADOOP Cluster, Hadoop Data Lake, Hadoop distributed file systems, Hadoop multi node cluster, HDFS, Hive, huge data reservoirs, huge volume of data, Ingestion, JDBC connector, JDBC protocol, Kafka, Kafka HDFS connector, Kafka to HDFS, Mainframe, mainframe dataset to HDFS, MapReduce, MapReduce distributed computing, migrating enterprise data, moving large amount of streaming data into HDFS, multi node cluster, multiple delimited text files, MySQL, Netezza, NoSql DB, NoSql Stores, Oracle, Oracle 11g Enterprise Edition, Oracle ATG Platform, parallel import process, parallel processing, pluggable mechanism, PostgreSQL, read the messages from Kafka topic, SQLServer, Sqoop, Sqoop installation, Strom, Using Kafka HDFS connectorComments Off

08JunJune 8, 2017

Why Lambda Architecture in Big Data Processing

Due to the exponential growth of digitization, the entire globe is creating minimum 2.5 Quintilian 2500000000000 Million) bytes of data every day and that we can denote as Big Data. Data generation is happening from everywhere starting from social media sites, various sensors, satellite, purchase transaction, Mobile, GPS signals and much more. With the advancement of technology, there is no sign of slowing down of data generation, instead it will grow in massive volume. All the major organizations, retailers,...

By Gautam GoswamiArchitecture, Data EngineeringApache Kafka, Apache Spark, Batch Data-processing Pipeline, batch layer, Big Data, Big Data Processing, big data technologies, Data ingestion, data pipeline, Data Pipelines, data processing pipeline, data warehousing systems, design framework, Digitization, fault tolerance, Flume Lambda sign λ, GPS signals, Hadoop Distributed File System, HDFS, Lambda, Lambda Architecture, Lambda Architecture in Big Data Processing, Lambda Architecture is a pluggable architecture, leveraging big data technologies, live streaming data, Mobile, Nathan Marz, persistence of data, pluggable architecture, purchase transaction, Quintilian bytes of data, satellite, sensors, Serving Layer, Speed layer, Streaming Data Pipeline, streaming data processing pipeline, streaming layer, Streaming or Speed layerComments Off

Tag - Data ingestion

Importance of Schema Registry on Kafka Based Data Streaming Pipelines

Data Absorption (Ingestion)

Distributed Incubator

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

Transfer structured data from Oracle to Hadoop storage system

Why Lambda Architecture in Big Data Processing

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