AtScale vs Zato
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics. | Connect, integrate and automate all of your systems, APIs and apps, including cloud and legacy ones, using an open-source integration platform in Python. ESB, SOA, REST, API and Cloud Integrations in Python. |
Multiple SQL-on-Hadoop Engine Support;
Access Data Where it Lays;
Built-in Support for Complex Data Types;
Single Drop-in Gateway Node Deployment | Integrate everything.
In Python.;
Connect, integrate and automate all of your systems, APIs and apps, including cloud and legacy ones, using an open-source integration platform in Python.;Say goodbye to integration challenges and hello to peace of mind. |
Statistics | |
GitHub Stars - | GitHub Stars 988 |
GitHub Forks - | GitHub Forks 246 |
Stacks 25 | Stacks 12 |
Followers 83 | Followers 24 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to AtScale, Zato?
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.
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
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.
