Azure Synapse vs Power BI

Overview

Power BI

Stacks994

Followers946

Votes29

Azure Synapse

Stacks104

Followers230

Votes10

Azure Synapse vs Power BI: What are the differences?

Azure Synapse and Power BI are both powerful Microsoft tools that offer analytics and business intelligence capabilities. Here are the key differences between them.

Scalability and Data Storage: Azure Synapse is primarily designed for large-scale enterprise data warehousing and analytics. It can handle massive amounts of structured and unstructured data and provides limitless scaling capabilities. On the other hand, Power BI is more focused on data visualization and reporting, and therefore, it has limited capabilities in storing and processing large volumes of data.
Data Integration and ETL: Azure Synapse offers comprehensive data integration and Extract, Transform, Load (ETL) capabilities. It provides tools like Azure Data Factory and Azure Databricks for data ingestion, transformation, and orchestration, and supports a wide range of data sources and connectors. Power BI, although it offers some data transformation capabilities, is more commonly used for data visualization and relies on external tools like Azure Data Factory for ETL processes.
Real-time Analytics: Azure Synapse enables real-time analytics through its integration with Azure Stream Analytics and Azure Event Hubs. It can process and analyze streaming data in real-time, providing valuable insights for immediate decision-making. On the other hand, Power BI is more suited for batch processing and analyzing historical data rather than real-time streaming data.
Machine Learning and AI Integration: Azure Synapse offers built-in integration with Azure Machine Learning and other AI services, allowing users to easily incorporate machine learning models and advanced analytics into their data workflows. Power BI, while it supports the consumption and visualization of machine learning models, lacks the robust integration capabilities of Azure Synapse.
Collaboration and Sharing: Power BI is well-known for its collaboration and sharing capabilities, allowing users to create interactive reports and dashboards and easily share them with others within or outside the organization. Azure Synapse, on the other hand, is more focused on data analytics and lacks the collaborative features provided by Power BI.
Cost and Pricing Model: Power BI offers a cost-effective pricing model based on the number of users and their access levels. It is suitable for small to medium-sized organizations with less complex data analysis requirements. Azure Synapse, on the other hand, has a more complex pricing model based on data storage, processing, and data movement, making it more suitable for large enterprises with extensive data analytics needs.

In summary, Azure Synapse is designed for large-scale data warehousing, ETL, real-time analytics, and machine learning integration, while Power BI is more focused on data visualization, collaboration, and sharing.

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Advice on Power BI, Azure Synapse

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	Azure Synapse
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.	It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
Get self-service analytics at enterprise scale; Use smart tools for strong results; Help protect your analytics data	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks 994	Stacks 104
Followers 946	Followers 230
Votes 29	Votes 10
Pros & Cons
Pros 18 Cross-filtering 4 Database visualisation 2 Intuitive and complete internal ETL 2 Powerful Calculation Engine 2 Access from anywhere	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Dictionary Size Limitation - CCI 1 Concurrency
Integrations
Microsoft Excel	No integrations available

What are some alternatives to Power BI, Azure Synapse?

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.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Related Comparisons

Azure Synapse vs Power BI: What are the differences?

Azure Synapse and Power BI are both powerful Microsoft tools that offer analytics and business intelligence capabilities. Here are the key differences between them.

Scalability and Data Storage: Azure Synapse is primarily designed for large-scale enterprise data warehousing and analytics. It can handle massive amounts of structured and unstructured data and provides limitless scaling capabilities. On the other hand, Power BI is more focused on data visualization and reporting, and therefore, it has limited capabilities in storing and processing large volumes of data.
Data Integration and ETL: Azure Synapse offers comprehensive data integration and Extract, Transform, Load (ETL) capabilities. It provides tools like Azure Data Factory and Azure Databricks for data ingestion, transformation, and orchestration, and supports a wide range of data sources and connectors. Power BI, although it offers some data transformation capabilities, is more commonly used for data visualization and relies on external tools like Azure Data Factory for ETL processes.
Real-time Analytics: Azure Synapse enables real-time analytics through its integration with Azure Stream Analytics and Azure Event Hubs. It can process and analyze streaming data in real-time, providing valuable insights for immediate decision-making. On the other hand, Power BI is more suited for batch processing and analyzing historical data rather than real-time streaming data.
Machine Learning and AI Integration: Azure Synapse offers built-in integration with Azure Machine Learning and other AI services, allowing users to easily incorporate machine learning models and advanced analytics into their data workflows. Power BI, while it supports the consumption and visualization of machine learning models, lacks the robust integration capabilities of Azure Synapse.
Collaboration and Sharing: Power BI is well-known for its collaboration and sharing capabilities, allowing users to create interactive reports and dashboards and easily share them with others within or outside the organization. Azure Synapse, on the other hand, is more focused on data analytics and lacks the collaborative features provided by Power BI.
Cost and Pricing Model: Power BI offers a cost-effective pricing model based on the number of users and their access levels. It is suitable for small to medium-sized organizations with less complex data analysis requirements. Azure Synapse, on the other hand, has a more complex pricing model based on data storage, processing, and data movement, making it more suitable for large enterprises with extensive data analytics needs.

Azure Synapse vs Power BI

Overview