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  5. Airbyte vs Azure Synapse

Airbyte vs Azure Synapse

OverviewComparisonAlternatives

Overview

Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10
Airbyte
Airbyte
Stacks105
Followers112
Votes5
GitHub Stars20.0K
Forks4.9K

Airbyte vs Azure Synapse: What are the differences?

Introduction

In this article, we will discuss the key differences between Airbyte and Azure Synapse.

  1. Data Integration Capabilities: Airbyte is an open-source data integration platform that specializes in moving data from various sources to desired destinations. It provides an extensive range of connectors, transformations, and data quality features to ensure seamless data integration. On the other hand, Azure Synapse is a fully managed data integration and analytics service offered by Microsoft. It combines data warehousing, big data processing, and data integration capabilities into a single platform.

  2. Scalability and Performance: Airbyte is designed to scale horizontally, allowing it to handle a growing volume of data sources and destinations. It can distribute data ingestion across multiple workers, ensuring efficient performance and scalability. In contrast, Azure Synapse leverages Microsoft's vast infrastructure to provide elastic scalability and high-performance data processing. It can handle large-scale data workloads and offers parallel processing capabilities for faster data transformations.

  3. Pricing Model: Airbyte is an open-source platform, which means it is free to use and modify. Users can deploy it on their own infrastructure or utilize cloud resources. Azure Synapse, on the other hand, follows a pay-as-you-go pricing model. Users are billed based on their usage of data storage, data movement, and data processing features. Additionally, Azure Synapse offers different pricing tiers to cater to various business needs.

  4. Ecosystem Integration: Airbyte supports a wide range of integrations with popular data tools and services. It can seamlessly connect with databases, cloud storages, data warehouses, and more. Moreover, Airbyte offers REST APIs and a standardized data model, making it compatible with various developer workflows. Azure Synapse integrates well with the Microsoft ecosystem, including Azure services, Power BI, and Azure Machine Learning. It provides a unified experience for data integration, analytics, and visualization within the Azure cloud environment.

  5. Enterprise-Grade Security and Governance: Airbyte focuses on providing secure data integration capabilities by implementing encryption, authentication, and access control measures. It allows users to manage sensitive data by ensuring compliance with industry-specific regulations such as GDPR. On the other hand, Azure Synapse offers robust security features such as data encryption, role-based access control, and Azure Active Directory integration. It also provides auditing and monitoring capabilities to ensure data governance and regulatory compliance.

  6. Advanced Analytics and Machine Learning: Azure Synapse goes beyond data integration and provides advanced analytics and machine learning capabilities. It offers built-in analytics tools and integration with Azure Machine Learning, allowing users to derive insights and build predictive models directly on their data. Airbyte primarily focuses on data integration and lacks the built-in analytics and machine learning capabilities present in Azure Synapse.

In summary, Airbyte is an open-source data integration platform with a strong focus on data movement and transformation capabilities, while Azure Synapse is a fully managed data integration and analytics service offered by Microsoft with a wider range of features and integration with the Azure ecosystem.

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Detailed Comparison

Azure Synapse
Azure Synapse
Airbyte
Airbyte

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.

It is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses lakes & DBs.

Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Scheduled updates; Manual full refresh; Real-time monitoring; Debugging autonomy; Optional normalized schemas; Full control over the data; Benefit from the long tail of connectors, and adapt them to your needs; Build connectors in the language of your choice, as they run in Docker containers
Statistics
GitHub Stars
-
GitHub Stars
20.0K
GitHub Forks
-
GitHub Forks
4.9K
Stacks
104
Stacks
105
Followers
230
Followers
112
Votes
10
Votes
5
Pros & Cons
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Concurrency
  • 1
    Dictionary Size Limitation - CCI
Pros
  • 1
    Multiple capabilities
  • 1
    Connect Multiple Sources
  • 1
    Change Data Capture
  • 1
    Easy to use
  • 1
    Free
Integrations
No integrations available
Greenhouse
Greenhouse
Google Cloud Platform
Google Cloud Platform
Mixpanel
Mixpanel
Google Analytics
Google Analytics
PostgreSQL
PostgreSQL
MySQL
MySQL
Shopify
Shopify
Amazon EC2
Amazon EC2
Zendesk
Zendesk
Stripe
Stripe

What are some alternatives to Azure Synapse, Airbyte?

Metabase

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

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

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

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

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

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

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

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

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

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.

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