Apache Spark vs AtScale

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

AtScale

Stacks25

Followers83

Votes0

Apache Spark vs AtScale: What are the differences?

Introduction:

Key differences between Apache Spark and AtScale are as follows:

Architecture: Apache Spark is a distributed computing system that operates as a processing engine for large-scale data processing, while AtScale is an analytics platform that enables companies to leverage their existing data infrastructure to provide a unified view for business intelligence. Apache Spark is designed for data analytics and machine learning, offering in-memory processing and fault tolerance, while AtScale focuses on providing a virtual data warehouse layer on top of existing data sources.
Use Case: Apache Spark is commonly used for data processing, machine learning, and real-time stream processing tasks, ideal for data scientists and engineers. On the other hand, AtScale is tailored for business users, enabling them to access and analyze data in a self-service manner without the need for complex data wrangling or SQL knowledge, making it more suitable for business intelligence and analytics teams.
Scalability: Apache Spark is highly scalable and can handle massive datasets by distributing computation across multiple nodes, making it suitable for big data processing. AtScale, on the other hand, does not offer the same level of scalability as Apache Spark in terms of processing huge volumes of data, as its focus is more on providing a unified view of data sources for analysis rather than parallel processing of large datasets.
Integration: Apache Spark integrates well with various data sources and tools, such as Hadoop, Kafka, and SQL databases, allowing for seamless data ingestion and processing. AtScale, on the other hand, focuses on providing a layer of abstraction for data sources, allowing users to access data from different platforms without the need for integration or transformation, simplifying the data access and analysis process.
Cost: Apache Spark is an open-source project, offering a cost-effective solution for organizations looking to process and analyze large volumes of data without the need for expensive proprietary software licenses. AtScale, however, is a commercial product with licensing fees, catering more to enterprise users looking for advanced analytics capabilities and support services.
Performance Optimization: Apache Spark provides advanced performance optimizations through features like caching, lazy evaluation, and in-memory processing, ensuring efficient data processing and computation. AtScale focuses more on providing a unified semantic layer for business users, optimizing query performance by translating user queries into efficient queries for the data sources underneath, ensuring fast and reliable data access for analysis.

In Summary, Apache Spark and AtScale differ in architecture, use case, scalability, integration, cost, and performance optimization, catering to different user needs in data processing and analytics.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Apache Spark, AtScale

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.

576k views576k

Comments

Detailed Comparison

Apache Spark	AtScale
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.	Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
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	Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 25
Followers 3.5K	Followers 83
Votes 140	Votes 0
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	No community feedback yet
Integrations
No integrations available	Python Amazon S3 Tableau Power BI Qlik Sense Azure Database for PostgreSQL

What are some alternatives to Apache Spark, AtScale?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

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.

Related Comparisons