Overview

Power BI Embedded

Stacks65

Followers147

Votes0

Azure Synapse

Stacks104

Followers230

Votes10

Azure Synapse vs Power BI Embedded: What are the differences?

# Comparison between Azure Synapse and Power BI Embedded

<Write Introduction here>

1. **Data Integration and Analysis Capabilities**: Azure Synapse is a comprehensive analytics service that enables integration, management, and analysis of data across the organization, including structured and unstructured data. On the other hand, Power BI Embedded is a platform-as-a-service offering that allows developers to embed Power BI reports and dashboards into custom applications.
   
2. **Scalability and Architecture**: Azure Synapse is designed for large-scale data processing and analytics, providing unlimited scalability to handle massive volumes of data. Power BI Embedded, on the other hand, is focused on embedding visual analytics within applications to provide interactive data insights to end-users.
   
3. **Data Processing and Transformation**: Azure Synapse offers powerful data processing and transformation capabilities with integrated Apache Spark and SQL engines for advanced analytics. Power BI Embedded, on the other hand, leverages the capabilities of Power BI Desktop and Power Query for data modeling and preparation.
   
4. **Cost Structure**: Azure Synapse follows a pay-as-you-go pricing model based on usage and data processing capacity, allowing organizations to scale resources based on requirements. Power BI Embedded, on the other hand, offers a capacity-based pricing model where organizations pay for the number of renders and API calls made by their applications.
   
5. **Deployment and Management**: Azure Synapse provides a unified workspace for data engineers, data scientists, and analysts to collaborate on data projects, with integrated security and governance features. Power BI Embedded focuses on easy deployment of interactive reports and dashboards within applications, with specific APIs for integration and customization.
   
6. **Target Audience and Use Cases**: Azure Synapse is geared towards organizations looking for an end-to-end analytics platform to unify data integration, data warehousing, and big data analytics. Power BI Embedded is ideal for developers seeking to enhance their applications with interactive visualizations and insights powered by Power BI.

In Summary, Azure Synapse and Power BI Embedded differ in terms of their data integration capabilities, scalability, data processing and transformation features, cost structure, deployment options, and target audience.

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Detailed Comparison

Power BI Embedded	Azure Synapse
Quickly and easily provide customer-facing reports, dashboards, and analytics in your own applications by using and branding it as your own. Reduce developer resources by automating the monitoring, management, and deployment of analytics, while getting full control of Power BI features and intelligent analytics.	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.
API;SDK;Reports;Dashboards	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks 65	Stacks 104
Followers 147	Followers 230
Votes 0	Votes 10
Pros & Cons
No community feedback yet	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Concurrency 1 Dictionary Size Limitation - CCI
Integrations
JavaScript Microsoft SharePoint	No integrations available

What are some alternatives to Power BI Embedded, 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.