Apache Spark vs Heroku Postgres

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Heroku Postgres

Stacks607

Followers314

Votes38

Apache Spark vs Heroku Postgres: What are the differences?

Apache Spark and Heroku Postgres are two popular technologies used in data processing and management. Apache Spark is a fast and general-purpose cluster computing system, while Heroku Postgres is a managed database service. The key differences between Apache Spark and Heroku Postgres are as follows:

Processing Framework vs. Database Service: Apache Spark is primarily used as a distributed data processing framework for big data analytics and machine learning applications. On the other hand, Heroku Postgres is a fully managed relational database service that is optimized for handling transactional data and providing scalable storage solutions.
Use Cases: Apache Spark is commonly used for processing large volumes of data, running complex data analytics algorithms, and building machine learning models. In contrast, Heroku Postgres is more suited for transactional applications, content management systems, and other data-driven web applications that require reliable database management.
Scalability: Apache Spark offers horizontal scalability by distributing data processing tasks across multiple nodes in a cluster, allowing users to handle massive datasets efficiently. Heroku Postgres, on the other hand, provides vertical scalability by offering different tiers of database instances with varying performance and storage capacities.
Data Storage: While Apache Spark does not provide built-in data storage capabilities, it can be integrated with various data sources such as Hadoop Distributed File System (HDFS), Apache HBase, and Apache Cassandra for storing processed data. Heroku Postgres, on the other hand, is designed specifically for storing and managing relational data in a secure and reliable manner.
Programming Language Support: Apache Spark supports multiple programming languages including Scala, Java, Python, and R, making it versatile for different use cases and data processing tasks. Heroku Postgres, on the other hand, is language-agnostic and can be accessed using standard SQL queries from any programming language that supports database connectivity.

In Summary, Apache Spark is a versatile data processing framework suited for big data analytics and machine learning applications, while Heroku Postgres is a reliable managed database service optimized for transactional web applications requiring scalable storage solutions.

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Advice on Apache Spark, Heroku Postgres

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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Comments

Jorge

Jan 15, 2020

Needs advice

Considering moving part of our PostgreSQL database infrastructure to the cloud, however, not quite sure between AWS, Heroku, Azure and Google cloud. Things to consider: The main reason is for backing up and centralize all our data in the cloud. With that in mind the main elements are: -Pricing for storage. -Small team. -No need for high throughput. -Support for docker swarm and Kubernetes.

51.8k views51.8k

Comments

Detailed Comparison

Apache Spark	Heroku Postgres
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.	Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.
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	High Availability;Rollback;Dataclips;Automated Health Checks
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 607
Followers 3.5K	Followers 314
Votes 140	Votes 38
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 29 Easy to setup 3 Follower databases 3 Dataclips for sharing queries 3 Extremely reliable Cons 2 Super expensive
Integrations
No integrations available	PostgreSQL Heroku

What are some alternatives to Apache Spark, Heroku Postgres?

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.

Amazon RDS for PostgreSQL

Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS.

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.

Related Comparisons

Apache Spark vs Heroku Postgres: What are the differences?

Processing Framework vs. Database Service: Apache Spark is primarily used as a distributed data processing framework for big data analytics and machine learning applications. On the other hand, Heroku Postgres is a fully managed relational database service that is optimized for handling transactional data and providing scalable storage solutions.
Use Cases: Apache Spark is commonly used for processing large volumes of data, running complex data analytics algorithms, and building machine learning models. In contrast, Heroku Postgres is more suited for transactional applications, content management systems, and other data-driven web applications that require reliable database management.
Scalability: Apache Spark offers horizontal scalability by distributing data processing tasks across multiple nodes in a cluster, allowing users to handle massive datasets efficiently. Heroku Postgres, on the other hand, provides vertical scalability by offering different tiers of database instances with varying performance and storage capacities.
Data Storage: While Apache Spark does not provide built-in data storage capabilities, it can be integrated with various data sources such as Hadoop Distributed File System (HDFS), Apache HBase, and Apache Cassandra for storing processed data. Heroku Postgres, on the other hand, is designed specifically for storing and managing relational data in a secure and reliable manner.
Programming Language Support: Apache Spark supports multiple programming languages including Scala, Java, Python, and R, making it versatile for different use cases and data processing tasks. Heroku Postgres, on the other hand, is language-agnostic and can be accessed using standard SQL queries from any programming language that supports database connectivity.

Apache Spark vs Heroku Postgres

Overview