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

CDAP vs Singer

OverviewComparisonAlternatives

Overview

CDAP
CDAP
Stacks41
Followers108
Votes0
Singer
Singer
Stacks21
Followers34
Votes2
GitHub Stars573
Forks132

CDAP vs Singer: What are the differences?

# Introduction
This Markdown code outlines the key differences between CDAP and Singer for website implementation.

1. **Architecture**: CDAP is an integrated data application platform that provides a unified view of data for processing and analytics, while Singer is a data ingestion framework that focuses on extracting data from various sources and loading it into data warehouses or lakes.
2. **Scalability**: CDAP is designed for enterprise-level scalability, offering features like data governance, security, and data lineage, whereas Singer is more lightweight and suitable for smaller data integration tasks.
3. **Data Transformation**: CDAP enables users to transform and enrich data within the platform using its built-in tools, whereas Singer primarily focuses on extracting and loading data with minimal transformation capabilities.
4. **Plugin Ecosystem**: CDAP has a rich ecosystem of plugins that extend its functionality for various data processing tasks, while Singer relies on a plugin-based architecture for connecting to different data sources and destinations.
5. **Real-time Processing**: CDAP supports real-time data processing and streaming capabilities, making it suitable for applications that require low latency, whereas Singer is more suited for batch processing jobs.
6. **Community Support**: CDAP has strong community support and active development, with a dedicated team behind the project, while Singer, although widely used, may have limited support and resources for troubleshooting and enhancements.

In Summary, the key differences between CDAP and Singer lie in their architecture, scalability, data transformation capabilities, plugin ecosystem, real-time processing support, and community backing.

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Detailed Comparison

CDAP
CDAP
Singer
Singer

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.

Singer powers data extraction and consolidation for all of your organization’s tools: advertising platforms, web analytics, payment processors, email service providers, marketing automation, databases, and more.

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
-
GitHub Stars
573
GitHub Forks
-
GitHub Forks
132
Stacks
41
Stacks
21
Followers
108
Followers
34
Votes
0
Votes
2
Pros & Cons
No community feedback yet
Pros
  • 1
    Open source
  • 1
    Multiple inputs "taps"
Integrations
Hadoop
Hadoop
GitLab
GitLab
FreshDesk
FreshDesk
Braintree
Braintree
HubSpot
HubSpot
Marketo
Marketo
Shippo
Shippo
Close.io
Close.io
Harvest
Harvest
Urban Airship
Urban Airship
FullStory
FullStory

What are some alternatives to CDAP, Singer?

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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