Apache Spark vs Singer

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Singer

Stacks21

Followers34

Votes2

GitHub Stars573

Forks132

Apache Spark vs Singer: What are the differences?

Introduction:

Apache Spark and Singer are two popular tools in the data processing domain. While both serve similar purposes, they have key differences that set them apart. Below are the main distinctions between Apache Spark and Singer.

Processing Framework: Apache Spark is a distributed computing system that can process large datasets across multiple nodes in a cluster, making it ideal for big data tasks. On the other hand, Singer is an open-source ETL (Extract, Transform, Load) tool that focuses on extracting data from various sources, transforming it, and loading it into desired destinations. While Spark is more versatile in terms of processing capabilities, Singer is specifically designed for data integration tasks.
Programming Language: Apache Spark primarily uses Scala, although it also supports programming in Java, Python, and R. Spark provides APIs for these languages, making it accessible to a wide range of developers. Singer, on the other hand, relies on a configuration file written in JSON to define data extraction and transformation tasks. This makes Singer easier to use for individuals with less programming experience.
Real-time Processing: Apache Spark is known for its ability to handle real-time data processing, thanks to its streaming capabilities using technologies like Spark Streaming and Structured Streaming. On the flip side, Singer is more suited for batch processing, where data is processed in large batches rather than in real-time. If real-time processing is a critical requirement for a project, Apache Spark would be the preferred choice over Singer.
Supported Data Sources: Apache Spark supports a wide range of data sources and formats, including traditional databases, data lakes, streaming data sources, and more. It can seamlessly integrate with various data storage solutions, making it a versatile choice for diverse data processing needs. In contrast, Singer specializes in extracting data from API endpoints, databases, and other sources using a pre-built set of taps, limiting its flexibility compared to Spark's broader data source support.
Scalability: Apache Spark is built for scalability, allowing users to scale their processing tasks horizontally by adding more nodes to the cluster. This distributed computing approach enables Spark to handle massive datasets efficiently. Singer, while capable of running on scalable infrastructure, may not offer the same level of scalability as Spark due to its focus on ETL orchestration rather than distributed data processing.
Community and Ecosystem: Apache Spark boasts a robust community and a vast ecosystem of tools and libraries that extend its capabilities for various use cases. This includes machine learning libraries like MLLib and graph processing capabilities with GraphX. Singer, while having an active community, may not have as extensive an ecosystem as Spark, limiting its adaptability for complex data processing tasks that require specialized tooling.

In Summary, Apache Spark excels in distributed computing, real-time processing, and scalability, making it ideal for big data and complex processing tasks, whereas Singer specializes in ETL tasks, particularly for data extraction and transformation from various sources in a batch processing environment.

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Advice on Apache Spark, Singer

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	Singer
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars 573
GitHub Forks 28.9K	GitHub Forks 132
Stacks 3.1K	Stacks 21
Followers 3.5K	Followers 34
Votes 140	Votes 2
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 1 Open source 1 Multiple inputs "taps"
Integrations
No integrations available	GitLab FreshDesk Braintree HubSpot Marketo Shippo Close.io Harvest Urban Airship FullStory

What are some alternatives to Apache Spark, Singer?

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.

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

Apache Spark vs Singer: What are the differences?

Introduction:

Processing Framework: Apache Spark is a distributed computing system that can process large datasets across multiple nodes in a cluster, making it ideal for big data tasks. On the other hand, Singer is an open-source ETL (Extract, Transform, Load) tool that focuses on extracting data from various sources, transforming it, and loading it into desired destinations. While Spark is more versatile in terms of processing capabilities, Singer is specifically designed for data integration tasks.
Programming Language: Apache Spark primarily uses Scala, although it also supports programming in Java, Python, and R. Spark provides APIs for these languages, making it accessible to a wide range of developers. Singer, on the other hand, relies on a configuration file written in JSON to define data extraction and transformation tasks. This makes Singer easier to use for individuals with less programming experience.
Real-time Processing: Apache Spark is known for its ability to handle real-time data processing, thanks to its streaming capabilities using technologies like Spark Streaming and Structured Streaming. On the flip side, Singer is more suited for batch processing, where data is processed in large batches rather than in real-time. If real-time processing is a critical requirement for a project, Apache Spark would be the preferred choice over Singer.
Supported Data Sources: Apache Spark supports a wide range of data sources and formats, including traditional databases, data lakes, streaming data sources, and more. It can seamlessly integrate with various data storage solutions, making it a versatile choice for diverse data processing needs. In contrast, Singer specializes in extracting data from API endpoints, databases, and other sources using a pre-built set of taps, limiting its flexibility compared to Spark's broader data source support.
Scalability: Apache Spark is built for scalability, allowing users to scale their processing tasks horizontally by adding more nodes to the cluster. This distributed computing approach enables Spark to handle massive datasets efficiently. Singer, while capable of running on scalable infrastructure, may not offer the same level of scalability as Spark due to its focus on ETL orchestration rather than distributed data processing.
Community and Ecosystem: Apache Spark boasts a robust community and a vast ecosystem of tools and libraries that extend its capabilities for various use cases. This includes machine learning libraries like MLLib and graph processing capabilities with GraphX. Singer, while having an active community, may not have as extensive an ecosystem as Spark, limiting its adaptability for complex data processing tasks that require specialized tooling.

Apache Spark vs Singer

Overview