Kudu vs Talend

Overview

Apache Kudu

Stacks71

Followers259

Votes10

GitHub Stars828

Forks282

Talend

Stacks297

Followers249

Votes0

Kudu vs Talend: What are the differences?

Developers describe Kudu as "Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform". A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data. On the other hand, Talend is detailed as "A single, unified suite for all integration needs". It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.

Kudu and Talend can be categorized as "Big Data" tools.

Kudu is an open source tool with 789 GitHub stars and 263 GitHub forks. Here's a link to Kudu's open source repository on GitHub.

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Advice on Apache Kudu, Talend

karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

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Comments

Detailed Comparison

Apache Kudu	Talend
A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.	It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.
Statistics
GitHub Stars 828	GitHub Stars -
GitHub Forks 282	GitHub Forks -
Stacks 71	Stacks 297
Followers 259	Followers 249
Votes 10	Votes 0
Pros & Cons
Pros 10 Realtime Analytics Cons 1 Restart time	No community feedback yet
Integrations
Hadoop	No integrations available

What are some alternatives to Apache Kudu, Talend?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.