AtScale vs Tableau

Overview

Tableau

Stacks1.3K

Followers1.4K

Votes8

AtScale

Stacks25

Followers83

Votes0

AtScale vs Tableau: What are the differences?

Data Virtualization: AtScale focuses on data virtualization, allowing users to query data without moving or copying it. This feature streamlines data access and ensures consistency across different systems. In contrast, Tableau requires data to be loaded into its software for analysis, which can lead to duplicated data and potential inconsistencies.
Aggregation: AtScale excels in handling large datasets by aggregating data at the source, ensuring fast query responses regardless of data size. On the other hand, Tableau may encounter performance issues when dealing with extensive data sets, as it processes information within its own environment.
Compatibility with Multiple Data Sources: AtScale supports a wide range of data sources and formats, enabling users to access and analyze diverse datasets seamlessly. In comparison, Tableau has limitations in data source compatibility, requiring additional tools or configurations to integrate data from different sources.
Advanced Analytics Capabilities: AtScale offers advanced analytics features such as machine learning algorithms and predictive analytics tools, empowering users to gain deeper insights into their data. Tableau, while robust in visual analytics, may lack the advanced analytical capabilities provided by AtScale.
Scalability: AtScale is designed to handle scalable workloads, making it suitable for enterprise-level data analysis with high performance and capacity. Tableau, while scalable to a certain extent, may encounter limitations in handling massive amounts of data and computations efficiently.
Governance and Security: AtScale provides robust governance and security features, allowing organizations to control access to data and ensure compliance with regulations. Compared to AtScale, Tableau may have fewer built-in governance and security functionalities, requiring additional measures to secure sensitive data effectively.

In Summary, AtScale and Tableau differ in terms of data virtualization, aggregation capabilities, data source compatibility, advanced analytics, scalability, and governance/security features.

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CLI (Node.js)

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Advice on Tableau, AtScale

Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

353k views353k

Comments

Wei

CTO at Flux Work

Jan 8, 2020

Decided

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

230k views230k

Comments

Detailed Comparison

Tableau	AtScale
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.	Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
Connect to data on prem or in the cloud—whether it’s big data, a SQL database, a spreadsheet, or cloud apps like Google Analytics and Salesforce. Access and combine disparate data without writing code. Power users can pivot, split, and manage metadata to optimize data sources. Analysis begins with data. Get more from yours with Tableau.; Exceptional analytics demand more than a pretty dashboard. Quickly build powerful calculations from existing data, drag and drop reference lines and forecasts, and review statistical summaries. Make your point with trend analyses, regressions, and correlations for tried and true statistical understanding. Ask new questions, spot trends, identify opportunities, and make data-driven decisions with confidence.; Answer the “where” as well as the “why.” Create interactive maps automatically. Built-in postal codes mean lightning-fast mapping for more than 50 countries worldwide. Use custom geocodes and territories for personalized regions, like sales areas. We designed Tableau maps specifically to help your data stand out.; Ditch the static slides for live stories that others can explore. Create a compelling narrative that empowers everyone you work with to ask their own questions, analyzing interactive visualizations with fresh data. Be part of a culture of data collaboration, extending the impact of your insights.	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
Stacks 1.3K	Stacks 25
Followers 1.4K	Followers 83
Votes 8	Votes 0
Pros & Cons
Pros 6 Capable of visualising billions of rows 1 Responsive 1 Intuitive and easy to learn Cons 3 Very expensive for small companies	No community feedback yet
Integrations
No integrations available	Python Amazon S3 Power BI Qlik Sense Azure Database for PostgreSQL

What are some alternatives to Tableau, 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.

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.

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AtScale vs Tableau: What are the differences?

Data Virtualization: AtScale focuses on data virtualization, allowing users to query data without moving or copying it. This feature streamlines data access and ensures consistency across different systems. In contrast, Tableau requires data to be loaded into its software for analysis, which can lead to duplicated data and potential inconsistencies.
Aggregation: AtScale excels in handling large datasets by aggregating data at the source, ensuring fast query responses regardless of data size. On the other hand, Tableau may encounter performance issues when dealing with extensive data sets, as it processes information within its own environment.
Compatibility with Multiple Data Sources: AtScale supports a wide range of data sources and formats, enabling users to access and analyze diverse datasets seamlessly. In comparison, Tableau has limitations in data source compatibility, requiring additional tools or configurations to integrate data from different sources.
Advanced Analytics Capabilities: AtScale offers advanced analytics features such as machine learning algorithms and predictive analytics tools, empowering users to gain deeper insights into their data. Tableau, while robust in visual analytics, may lack the advanced analytical capabilities provided by AtScale.
Scalability: AtScale is designed to handle scalable workloads, making it suitable for enterprise-level data analysis with high performance and capacity. Tableau, while scalable to a certain extent, may encounter limitations in handling massive amounts of data and computations efficiently.
Governance and Security: AtScale provides robust governance and security features, allowing organizations to control access to data and ensure compliance with regulations. Compared to AtScale, Tableau may have fewer built-in governance and security functionalities, requiring additional measures to secure sensitive data effectively.

In Summary, AtScale and Tableau differ in terms of data virtualization, aggregation capabilities, data source compatibility, advanced analytics, scalability, and governance/security features.