StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Azure Synapse vs Treasure Data

Azure Synapse vs Treasure Data

OverviewComparisonAlternatives

Overview

Treasure Data
Treasure Data
Stacks28
Followers44
Votes5
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Azure Synapse vs Treasure Data: What are the differences?

Introduction:

Azure Synapse and Treasure Data are both data platforms that offer capabilities for data analytics, processing, and storage. However, there are key differences between the two platforms that set them apart in terms of functionality, scalability, and ease of use.

  1. Architecture: Azure Synapse is an integrated analytics service that combines data integration, enterprise data warehousing, and big data analytics. It offers seamless integration with various Azure services such as Azure Data Lake Storage, Azure Blob Storage, and Azure Data Factory. In contrast, Treasure Data is a customer data platform that specializes in processing and analyzing large volumes of customer data to drive personalized marketing campaigns and customer insights.

  2. Scalability: Azure Synapse is designed to scale seamlessly as data volume and processing needs grow, with the ability to distribute query processing across multiple nodes. Treasure Data, on the other hand, is optimized for handling large-scale data processing and analytics for customer data, making it well-suited for industries such as retail, e-commerce, and online services.

  3. Data Sources: Azure Synapse supports a wide range of data sources including structured, semi-structured, and unstructured data from different data platforms and types. Treasure Data, on the other hand, focuses on aggregating and analyzing customer data from various sources such as CRM systems, web analytics, and IoT devices.

  4. Ease of Use: Azure Synapse provides a unified interface for data engineers, data scientists, and business analysts to collaborate on data projects. It offers built-in tools for data preparation, data exploration, and data visualization. Treasure Data, on the other hand, is known for its user-friendly interface and simplicity in setting up data pipelines for customer data analysis.

  5. Security and Compliance: Azure Synapse offers robust security features such as data encryption, access control, and compliance certifications to meet industry-specific regulatory requirements. Treasure Data also prioritizes data security and compliance by offering features like data encryption, access controls, and GDPR compliance tools specifically tailored to customer data protection.

  6. Cost: Azure Synapse pricing is based on the amount of data processed and stored, making it a cost-effective solution for organizations with varying data processing needs. Treasure Data pricing is based on data volume and processing time, with customizable plans to suit different customer data analytics requirements.

In Summary, Azure Synapse and Treasure Data offer distinct advantages in terms of architecture, scalability, data sources, ease of use, security, and cost, catering to different data processing and analytics needs in the industry.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Treasure Data
Treasure Data
Azure Synapse
Azure Synapse

Treasure Data's Big Data as-a-Service cloud platform enables data-driven businesses to focus their precious development resources on their applications, not on mundane, time-consuming integration and operational tasks. The Treasure Data Cloud Data Warehouse service offers an affordable, quick-to-implement and easy-to-use big data option that does not require specialized IT resources, making big data analytics available to the mass market.

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.

Instant Integration- Using td-agent, you can start importing your data from existing log files, web and packaged applications right away.;Streaming or Batch?- You choose! Our data collection tool, td-agent, enables you to stream or batch your data to the cloud in JSON format.;Secure Upload- The connection between td-agent and the cloud is SSL-encrypted, ensuring secure transfer of your data.;Availability- Our best-in-class, multi-tenant architecture uses Amazon S3 to ensure 24x7 availability and automatic replication.;Columnar Database- Our columnar database not only delivers blinding performance, it also compresses data to 5 to 10 percent of its original size.;Schema Free- Unlike traditional databases – even cloud databases – Treasure Data allows you to change your data schema anytime.;SQL-like Query Language- Query your data using our SQL-like language.;BI Tools Connectivity- Treasure Data allows you to use your existing BI/visualization tools (e.g. JasperSoft, Pentaho, Talend, Indicee, Metric Insights) using our JDBC driver.;Enterprise-level Service and Support;No Lock-in- We provide a one-line command to let you export your data anywhere you choose, whenever you choose.
Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks
28
Stacks
104
Followers
44
Followers
230
Votes
5
Votes
10
Pros & Cons
Pros
  • 2
    Scaleability, less overhead
  • 2
    Makes it easy to ingest all data from different inputs
  • 1
    Responsive to our business requirements, great support
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Dictionary Size Limitation - CCI
  • 1
    Concurrency
Integrations
Amazon EC2
Amazon EC2
G Suite
G Suite
Heroku
Heroku
Engine Yard Cloud
Engine Yard Cloud
Red Hat OpenShift
Red Hat OpenShift
cloudControl
cloudControl
No integrations available

What are some alternatives to Treasure Data, Azure Synapse?

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.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase