CDAP vs Impala

Overview

Apache Impala

Stacks145

Followers301

Votes18

GitHub Stars34

Forks33

CDAP

Stacks41

Followers108

Votes0

CDAP vs Impala: What are the differences?

Developers describe CDAP as "Open source virtualization platform for Hadoop data and apps". Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements. On the other hand, Impala is detailed as "Real-time Query for Hadoop". 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.

CDAP and Impala can be categorized as "Big Data" tools.

Some of the features offered by CDAP are:

Streams for data ingestion
Reusable libraries for common Big Data access patterns
Data available to multiple applications and different paradigms

On the other hand, Impala provides the following key features:

Do BI-style Queries on Hadoop
Unify Your Infrastructure
Implement Quickly

CDAP and Impala are both open source tools. Impala with 2.18K GitHub stars and 824 forks on GitHub appears to be more popular than CDAP with 346 GitHub stars and 178 GitHub forks.

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Advice on Apache Impala, CDAP

Asha

Jan 11, 2022

Needs adviceon

Java

Apache Kudu

Apache Impala

I have been working on a Java application to demonstrate the latency for the select/insert/update operations on KUDU storage using Apache Kudu API - Java based client. I have a few queries about using Apache Kudu API

Do we have JDBC wrapper to use Apache Kudu API for getting connection to Kudu masters with connection pool mechanism and all DB operations?
Does Apache KuduAPI supports order by, group by, and aggregate functions? if yes, how to implement these functions using Kudu APIs.
How can we add kudu predicates to Kudu update operation? if yes, how?
Does Apache Kudu API supports batch insertion (execute the Kudu Insert for multiple rows at one go instead of row by row)? (like Kudusession.apply(List<Insert>);)
Does Apache Kudu API support join on tables?
which tool is preferred over others (Apache Impala /Kudu API) for read and update/insert DB operations?

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Detailed Comparison

Apache Impala	CDAP
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.	Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements.
Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse	Streams for data ingestion;Reusable libraries for common Big Data access patterns;Data available to multiple applications and different paradigms;Framework level guarantees;Full development lifecycle and production deployment;Standardization of applications across programming paradigms
Statistics
GitHub Stars 34	GitHub Stars -
GitHub Forks 33	GitHub Forks -
Stacks 145	Stacks 41
Followers 301	Followers 108
Votes 18	Votes 0
Pros & Cons
Pros 11 Super fast 1 High Performance 1 Distributed 1 Scalability 1 Replication	No community feedback yet
Integrations
Hadoop Mode Redash Apache Kudu	Hadoop

What are some alternatives to Apache Impala, CDAP?

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.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Some of the features offered by CDAP are:

Streams for data ingestion
Reusable libraries for common Big Data access patterns
Data available to multiple applications and different paradigms

On the other hand, Impala provides the following key features:

Do BI-style Queries on Hadoop
Unify Your Infrastructure
Implement Quickly

CDAP and Impala are both open source tools. Impala with 2.18K GitHub stars and 824 forks on GitHub appears to be more popular than CDAP with 346 GitHub stars and 178 GitHub forks.

CDAP vs Impala