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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. CDAP vs Delta Lake

CDAP vs Delta Lake

OverviewComparisonAlternatives

Overview

CDAP
CDAP
Stacks41
Followers108
Votes0
Delta Lake
Delta Lake
Stacks105
Followers315
Votes0
GitHub Stars8.4K
Forks1.9K

CDAP vs Delta Lake: 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, Delta Lake is detailed as "Reliable Data Lakes at Scale". An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

CDAP and Delta Lake can be primarily classified 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, Delta Lake provides the following key features:

  • ACID Transactions
  • Scalable Metadata Handling
  • Time Travel (data versioning)

CDAP and Delta Lake are both open source tools. It seems that Delta Lake with 1.26K GitHub stars and 210 forks on GitHub has more adoption than CDAP with 346 GitHub stars and 178 GitHub forks.

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Detailed Comparison

CDAP
CDAP
Delta Lake
Delta Lake

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.

An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

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
ACID Transactions; Scalable Metadata Handling; Time Travel (data versioning); Open Format; Unified Batch and Streaming Source and Sink; Schema Enforcement; Schema Evolution; 100% Compatible with Apache Spark API
Statistics
GitHub Stars
-
GitHub Stars
8.4K
GitHub Forks
-
GitHub Forks
1.9K
Stacks
41
Stacks
105
Followers
108
Followers
315
Votes
0
Votes
0
Integrations
Hadoop
Hadoop
Apache Spark
Apache Spark
Hadoop
Hadoop
Amazon S3
Amazon S3

What are some alternatives to CDAP, Delta Lake?

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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

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

Splunk

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

Apache Impala

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.

Vertica

Vertica

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

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