AtScale vs Power BI

Overview

Power BI

Stacks991

Followers946

Votes29

AtScale

Stacks25

Followers83

Votes0

AtScale vs Power BI: What are the differences?

Deployment: AtScale is a cloud-based solution, whereas Power BI can be deployed on cloud, on-premises, or a combination of both, providing more flexibility in deployment options.
Data Source Connectivity: AtScale focuses on connecting to large-scale data sources like Hadoop, Google BigQuery, and Snowflake, while Power BI offers a wide range of connectors to various data sources including SQL databases, Excel files, and cloud services.
Aggregation and Calculation: AtScale specializes in providing complex aggregation and calculation capabilities on big data sets without moving the data, while Power BI offers a user-friendly interface for creating interactive visualizations and reports that can be easily understood by non-technical users.
Scalability: AtScale is designed for handling large amounts of data and complex queries efficiently, making it suitable for enterprise-level analytics, whereas Power BI is more user-friendly and easier to use for small to medium-sized businesses.
Cost: AtScale may require a higher initial investment due to its focus on enterprise-level features and scalability, while Power BI offers more cost-effective options for organizations with varying budget constraints.

In Summary, AtScale and Power BI differ in deployment options, data source connectivity, aggregation capabilities, scalability, and cost considerations.

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Advice on Power BI, 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.

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Detailed Comparison

Power BI	AtScale
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.	Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
Get self-service analytics at enterprise scale; Use smart tools for strong results; Help protect your analytics data	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 991	Stacks 25
Followers 946	Followers 83
Votes 29	Votes 0
Pros & Cons
Pros 18 Cross-filtering 4 Database visualisation 2 Powerful Calculation Engine 2 Intuitive and complete internal ETL 2 Access from anywhere	No community feedback yet
Integrations
Microsoft Excel	Python Amazon S3 Tableau Qlik Sense Azure Database for PostgreSQL

What are some alternatives to Power BI, 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.

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

AtScale vs Power BI: What are the differences?

Deployment: AtScale is a cloud-based solution, whereas Power BI can be deployed on cloud, on-premises, or a combination of both, providing more flexibility in deployment options.
Data Source Connectivity: AtScale focuses on connecting to large-scale data sources like Hadoop, Google BigQuery, and Snowflake, while Power BI offers a wide range of connectors to various data sources including SQL databases, Excel files, and cloud services.
Aggregation and Calculation: AtScale specializes in providing complex aggregation and calculation capabilities on big data sets without moving the data, while Power BI offers a user-friendly interface for creating interactive visualizations and reports that can be easily understood by non-technical users.
Scalability: AtScale is designed for handling large amounts of data and complex queries efficiently, making it suitable for enterprise-level analytics, whereas Power BI is more user-friendly and easier to use for small to medium-sized businesses.
Cost: AtScale may require a higher initial investment due to its focus on enterprise-level features and scalability, while Power BI offers more cost-effective options for organizations with varying budget constraints.

In Summary, AtScale and Power BI differ in deployment options, data source connectivity, aggregation capabilities, scalability, and cost considerations.

AtScale vs Power BI

Overview