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Top 18 Data Ingestion Tools
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Top 18 Data Ingestion Tools

Top 18 Data Ingestion Tools
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Data Ingestion Tools : The process of importing, transferring, loading and processing data for later use or storage in a database is called Data ingestion and this involves loading data from a variety of sources, altering and modification of individual files and formatting them to fit into a larger document. Data ingestion can be continuous, asynchronous, real-time or batched and the source and the destination may also have different format or protocol, which will require some type of transformation or conversion. Amazon Kinesis, Apache Flume, Apache Kafka, Apache NIFI, Apache Samza, Apache Sqoop, Apache Storm, DataTorrent, Gobblin, Syncsort, Wavefront, Cloudera Morphlines, White Elephant, Apache Chukwa, Fluentd, Heka, Scribe and Databus are the top data ingestion tools.

Top Data Ingestion Tools: Trending

Top Data Ingestion Tools

Amazon Kinesis, Apache Flume, Apache Kafka, Apache NIFI, Apache Samza, Apache Sqoop, Apache Storm, DataTorrent, Gobblin, Syncsort, Wavefront,Cloudera Morphlines, White Elephant, Apache Chukwa, Fluentd, Heka, Scribe and Databus are the top data ingestion tools

1

Amazon Kinesis

Amazon Kinesis is a fully managed, cloud-based service for real-time data processing over large, distributed data streams. Amazon Kinesis can continuously capture and store terabytes of data per hour from hundreds of thousands of sources such as website clickstreams, financial transactions, social media feeds, IT logs, and location-tracking events. Amazon Kinesis enables data to be collected, stored, and processed continuously for Web applications, mobile devices, wearables, industrial sensors,etc. Web applications, mobile devices, wearables, industrial sensors, and many software applications and services can generate staggering amounts of streaming data – sometimes TBs per hour – that need to be collected, stored,…

Amazon Kinesis

2

Apache Flume

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application. Features include New in-memory channel that can spill to disk, A new dataset sink that use Kite API to write data to HDFS and HBase, Support for Elastic Search HTTP API in Elastic Search Sink and Much faster replay…

Apache Flume

3

Apache Kafka

Apache Kafka is an open-source message broker project to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than…

Apache Kafka

4

Apache NIFI

Apache NIFI supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. Some of the high-level capabilities of Apache NiFi include Web-based user interface, Seamless experience between design, control, feedback, and monitoring, data Provenance, SSL, SSH, HTTPS, encrypted content, etc, pluggable role-based authentication/authorization.Apache nifi is highly configurable with loss tolerant vs guaranteed delivery, low latency vs high throughput, dynamic prioritization, flow can be modified at runtime, back pressure.

Apache NIFI

5

Apache Samza

Apache Samza is a distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management. Unlike most low-level messaging system APIs, Samza provides a very simple callback-based “process message” API comparable to MapReduce. Samza manages snapshotting and restoration of a stream processor’s state. When the processor is restarted, Samza restores its state to a consistent snapshot. Samza is built to handle large amounts of state (many gigabytes per partition). Whenever a machine in the cluster fails, Samza works with YARN to transparently migrate your tasks to another…

Apache Samza

6

Apache Sqoop

Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.Sqoop supports incremental loads of a single table or a free form SQL query, saved jobs which can be run multiple times to import updates made to a database since the last import. Imports can also be used to populate tables in Hive or HBase.Exports can be used to put data from Hadoop into a relational database. Sqoop got the name from sql+hadoop

Apache Sqoop

7

Apache Storm

Apache Storm is a distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more.Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. Storm integrates with…

Apache Storm

8

DataTorrent

DataTorrent is the leader in real-time big data analytics. DataTorrent RTS provide high performing, fault tolerant unified architecture for both data in motion and data at rest. The engine provides a complete set of system services freeing the developer to focus on business logic. The platform is capable of processing billions of events per second and recovering from node outages with no data loss and no human intervention DataTorrent RTS is proven in production environments to reduce time to market, development costs and operational expenditures for Fortune 100 and leading Internet companies. DataTorrent RTS provides pre-built connectors for the most…

DataTorrent

9

Gobblin

Gobblin is a universal data ingestion framework for extracting, transforming, and loading large volume of data from a variety of data sources, such as databases, rest APIs, FTP/SFTP servers, filers, etc., onto Hadoop. Gobblin handles the common routine tasks required for all data ingestion ETLs, including job, task scheduling, task partitioning, error handling, state management, data quality checking, data publishing, etc.Gobblin ingests data from different data sources in the same execution framework, and manages metadata of different sources all in one place. This, combined with other features such as auto scalability, fault tolerance, data quality assurance, extensibility, and the ability…

Gobblin

10

Syncsort

Syncsort provides enterprise software that allows organizations to collect, integrate, sort and distribute more data in less time, with fewer resources and lower costs. Syncsort software provides specialized solutions spanning “Big Iron to Big Data,” including next gen analytical platforms such as Hadoop, cloud, and Splunk. Syncsort offers fast, secure, enterprise grade products to help the world’s leading organizations unleash the power of Big Data. With Syncsort, you can design your data applications once and deploy anywhere: from Windows, Unix & Linux to Hadoop; on premises or in the Cloud. Syncsort DMX-h was designed from the ground up for Hadoop…

Syncsort

11

Wavefront

Wavefront is a hosted platform for ingesting, storing, visualizing and alerting on metric data. It is based on a stream processing approach invented at Google which allows engineers to manipulate metric data with unparalleled power. Wavefront makes analytics easy, yet powerful. Our query language allows time series data to be manipulated in ways that have never been seen before. The language is easy-to-understand, yet powerful enough to deal with high-dimensional data. Wavefront can ingest millions of data points per second. Leveraging an intuitive query language, you can manipulate data in real-time and deliver on actionable insights. This helps to address…

Wavefront

12.Cloudera Morphlines

Cloudera Morphlines is an open source framework that reduces the time and skills necessary to build and change Hadoop ETL stream processing applications that extract, transform and load data into Apache Solr, Enterprise Data Warehouses, HDFS, HBase or Analytic Online Dashboards.

Cloudera Morphlines

13.White Elephant

White Elephant is a Hadoop log aggregator and dashboard which enables visualization of Hadoop cluster utilization across users

White Elephant

14.Apache Chukwa

Chukwa is an open source data collection system for monitoring large distributed systems. Chukwa is built on top of the Hadoop Distributed File System (HDFS) and Map/Reduce framework and inherits Hadoop’s scalability and robustness. Chukwa also includes a flexible and powerful toolkit for displaying, monitoring and analyzing results to make the best use of the collected data

Apache Chukwa

15.Fluentd

Fluentd is an open source data collector for unified logging layer. Fluentd allows to unify data collection and consumption for a better use and understanding of data.Fluentd decouples data sources from backend systems by providing a unified logging layer in between.

Fluentd

Fluentd

16.Heka

Heka is a tool for collecting and collating data from a number of different sources, performing “in-flight” processing of collected data, and delivering the results to any number of destinations for further analysis.

Heka

17.Scribe

Scribe is a server for aggregating log data that’s streamed in real time from clients. It is designed to be scalable and reliable.

Scribe

18.Databus

Databus provides a timeline-consistent stream of change capture events for a database. It enables applications to watch a database, view and process updates in near real-time. Databus provides a complete after-image of every new/changed record as well as deletes, while maintaining timeline consistency and transactional boundaries. The application integration is decoupled from the source database, and each application integration is isolated, which allows for parallel development and rapid innovation.

Databus

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