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  5. Azure Synapse vs Fivetran

Azure Synapse vs Fivetran

OverviewComparisonAlternatives

Overview

Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10
Fivetran
Fivetran
Stacks116
Followers119
Votes0

Azure Synapse vs Fivetran: What are the differences?

Introduction

Azure Synapse and Fivetran are both popular tools used in the field of data integration and analytics. While they have similar objectives, there are key differences between the two platforms.

  1. Scalability and Flexibility: Azure Synapse provides a highly scalable and flexible environment for data integration and analytics. With its integrated analytics service and ability to handle large datasets, it offers a wide range of capabilities to handle complex data integration tasks. On the other hand, Fivetran focuses primarily on data integration by offering automated data pipeline solutions, making it easier to transfer and sync data between various sources and destinations.

  2. In-depth Analytics Capabilities: Azure Synapse, being a unified analytics platform, offers powerful analytics capabilities such as data warehousing, big data processing, and machine learning. It provides tools for data exploration, visualization, and predictive analytics, enabling organizations to extract meaningful insights from their data. Fivetran, on the other hand, focuses more on data integration and does not directly provide in-depth analytics capabilities. It primarily focuses on ETL (Extract, Transform, Load) processes and data replication.

  3. Cloud Provider Dependency: Azure Synapse is a fully managed cloud service provided by Microsoft Azure, making it highly dependent on the Azure ecosystem. It leverages the Azure infrastructure and services, enabling seamless integration with other Azure services such as Azure Data Lake Storage and Azure Machine Learning. Fivetran, on the other hand, is a cloud-native service that can be deployed on various cloud providers like AWS, Google Cloud, and Azure. This provides more flexibility for organizations to choose their preferred cloud environment.

  4. Enterprise-level Security and Compliance: Azure Synapse offers robust security features and compliance certifications. It includes advanced threat protection, data encryption, and access control mechanisms to ensure data privacy and protection. Additionally, it complies with various industry standards and regulations such as GDPR and HIPAA. Fivetran also offers data security features but may not provide the same level of enterprise-grade security and compliance measures as Azure Synapse.

  5. Automation and Maintenance: Azure Synapse provides a managed service approach, which means that the platform handles the infrastructure management, updates, and maintenance tasks. This allows organizations to focus on their data analytics and integration tasks without worrying about the underlying infrastructure. In contrast, Fivetran offers a fully automated data pipeline solution, which takes care of ETL processes, data transformations, and data synchronization. This automation reduces the need for manual intervention and streamlines the data integration workflow.

  6. Pricing Model: Azure Synapse offers a pay-as-you-go pricing model, which provides flexibility for organizations to scale their resources based on their needs. The pricing is based on the usage of different components such as storage, compute, and data transfer. Fivetran, on the other hand, follows a subscription-based pricing model, where the pricing is based on the number of data sources and the data volume processed. Organizations need to consider their data integration requirements and budget constraints while choosing between the two platforms.

In summary, Azure Synapse provides a highly scalable and integrated analytics platform with in-depth analytics capabilities, strong security features, and close integration with the Azure ecosystem. Fivetran, on the other hand, focuses primarily on data integration with automated data pipeline solutions and supports multiple cloud providers, offering flexibility and ease of use.

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Detailed Comparison

Azure Synapse
Azure Synapse
Fivetran
Fivetran

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 helps you centralize data from disparate sources which you can manage directly from your browser. We extract your data and load it into your data destination.

Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Prebuilt connectors; Ready-to-query schemas; Automated schema migrations; Fully managed data; SQL-based transformations
Statistics
Stacks
104
Stacks
116
Followers
230
Followers
119
Votes
10
Votes
0
Pros & Cons
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Concurrency
  • 1
    Dictionary Size Limitation - CCI
No community feedback yet
Integrations
No integrations available
Amazon DynamoDB
Amazon DynamoDB
AWS Lambda
AWS Lambda
Mailchimp
Mailchimp
Amazon S3
Amazon S3

What are some alternatives to Azure Synapse, Fivetran?

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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