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IBM Netezza VS Azure Synapse Analytics

Compare IBM Netezza VS Azure Synapse Analytics and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

IBM Netezza logo IBM Netezza

Netezza is a powerful platform that changed the world of data warehousing by introducing one of the world’ first data warehouse appliances.

Azure Synapse Analytics logo Azure Synapse Analytics

Get started with Azure SQL Data Warehouse for an enterprise-class SQL Server experience. Cloud data warehouses offer flexibility, scalability, and big data insights.
  • IBM Netezza Landing page
    Landing page //
    2023-08-18
  • Azure Synapse Analytics Landing page
    Landing page //
    2023-03-23

IBM Netezza features and specs

  • High Performance
    IBM Netezza is known for its high-speed processing capabilities, which allow it to handle large volumes of data efficiently and deliver quick query responses.
  • Ease of Use
    The platform offers a user-friendly interface and SQL compatibility, making it accessible to data analysts and reducing the learning curve for new users.
  • Scalability
    Netezza can scale horizontally to accommodate growing data needs, making it suitable for businesses of various sizes that anticipate growth in their data requirements.
  • Integrated Analytics
    It provides integrated analytics capabilities, allowing users to perform complex data analysis directly within the database, reducing the need for separate analytics tools.
  • Robust Security
    IBM Netezza includes advanced security features, such as data encryption and user access controls, to protect sensitive data and ensure compliance with regulatory standards.

Possible disadvantages of IBM Netezza

  • Cost
    IBM Netezza can be expensive to implement and maintain, especially for smaller organizations with limited budgets, due to its hardware and licensing requirements.
  • Limited Flexibility
    The system has certain constraints in terms of customization and flexibility, which may limit how it can be tailored to specific business needs.
  • Complexity in Migration
    Migrating to or from Netezza can be complex and time-consuming, posing challenges during integration with existing data frameworks or transitioning to newer platforms.
  • Dependency on IBM Ecosystem
    Organizations using Netezza may become heavily reliant on the IBM ecosystem, which can limit flexibility and options in terms of using complementary tools and technologies from other vendors.
  • Potential Overhead
    Managing and maintaining a Netezza environment may require specialized skills and resources, potentially creating additional overhead for IT departments.

Azure Synapse Analytics features and specs

  • Integration
    Azure Synapse Analytics integrates with other Azure services like Azure Data Lake Storage, Power BI, and Azure Machine Learning, enabling seamless data movement and business intelligence processes.
  • Scalability
    It allows on-demand scalability, both horizontally and vertically, providing the flexibility to handle workloads of any size efficiently.
  • Unified Experience
    Offers a unified interface for data ingestion, preparation, management, and serving, simplifying data operations and reducing the need for multiple tools.
  • Advanced Security
    Includes robust security features like encryption, network protection, and advanced threat protection to ensure data security and compliance.
  • Serverless and Dedicated Options
    Provides both serverless and dedicated resource models, allowing businesses to optimize their costs by selecting the appropriate compute resources for their needs.

Possible disadvantages of Azure Synapse Analytics

  • Complexity
    The comprehensive range of features and tools can lead to a steep learning curve and complexity in setup and management for new users.
  • Cost Management
    Although flexibility is offered, managing and predicting costs can be challenging, especially in serverless scenarios where usage might fluctuate.
  • Resource Limitations
    Despite its scalability, there might be certain limitations in terms of data size or query complexity compared to some on-premises solutions.
  • Dependency on Internet Connectivity
    As a cloud-based solution, it requires stable and reliable internet connectivity, which may not be available in all regions or circumstances.
  • Integration Learning Curve
    While integration is a strength, mastering the integration with various Azure services and third-party tools can require substantial time and effort.

IBM Netezza videos

+ Add

Netezza Overview

More videos:

  • Review - Explain about Netezza
  • Review - Get to know the IBM Netezza Performance Server

Azure Synapse Analytics videos

+ Add

Azure Synapse Analytics - Next-gen Azure SQL Data Warehouse

More videos:

  • Review - Is Azure SQL Data Warehouse the Right SQL Platform for You?

Category Popularity

0-100% (relative to IBM Netezza and Azure Synapse Analytics)

IBM Netezza

Azure Synapse Analytics

Databases
100 100%
Databases
0% 0
Office & Productivity
0 0%
Office & Productivity
100% 100
Big Data
100 100%
Big Data
0% 0
Development
24 24%
Development
76% 76

User comments

Share your experience with using IBM Netezza and Azure Synapse Analytics. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare IBM Netezza and Azure Synapse Analytics

IBM Netezza Reviews

16 Top Big Data Analytics Tools You Should Know About
The Netezza Performance Server data warehouse system includes SQL that is known as IBM Netezza Structured Query Language (SQL). We can use SQL commands to create and manage the Netezza databases, user access, and permissions for the database. It can also be used to query and modify the contents of the databases.
Source: www.analytixlabs.co.in

Azure Synapse Analytics Reviews

Data Warehouse Tools
Azure Synapse Analytics (formerly Azure Data Warehouse) is a cloud-native data warehouse integrated with other Azure services. It unifies data warehousing and big data analytics for comprehensive insights, offering visually interactive tools for user-friendly data exploration.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
Azure Synapse analytics is scalable for large data tables based on its distributed computing. It relies on the MPP (mentioned in the beginning, revisit if you did not grasp it) to quickly run high volumes of complex queries across multiple nodes. With Synapse, there’s an extra emphasis on security and privacy.
Source: geekflare.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
Azure SQL Data Warehouse, now subsumed by Azure Synapse Analytics, brings together the worlds of big data analytics and enterprise data warehousing. Over the years, Azure has made a name for enabling the seamless transfer of data between on-premise and cloud ecosystems.
Source: blog.panoply.io

Social recommendations and mentions

Based on our record, Azure Synapse Analytics seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

IBM Netezza mentions (0)

We have not tracked any mentions of IBM Netezza yet. Tracking of IBM Netezza recommendations started around Mar 2021.

Azure Synapse Analytics mentions (4)

  • DbVisualizer 24.2: A Complete Review
    Azure Synapse Analytics: DbVisualizer now has extended support for dedicated and serverless SQL pools in Azure Synapse Analytics. That includes support for database-scoped credentials, external file formats and data sources, and external tables. For more information, see the Azure Synapse Dedicated and Azure Synapse Serverless pages on the official site. - Source: dev.to / 8 months ago
  • Deploying a Data Warehouse with Pulumi and Amazon Redshift
    A data warehouse is a specialized database that's purpose built for gathering and analyzing data. Unlike general-purpose databases like MySQL or PostgreSQL, which are designed to meet the real-time performance and transactional needs of applications, a data warehouse is designed to collect and process the data produced by those applications, collectively and over time, to help you gain insight from it. Examples of... - Source: dev.to / over 2 years ago
  • [WSJ] Facebook Parent Meta Expected to Post Slowest Revenue Growth Since IPO
    You don't run into these kinds of problems with other tools, like the ones I mentioned. I've never tried the Azure ones, but my gut says they may have some scaling issues (synapse analytics looks promising but I have no experience with it). Source: about 3 years ago
  • The Difference Between Data Warehouses, Data Lakes, and Data Lakehouses.
    Popular managed cloud data warehouse solutions include Azure Synapse Analytics, Azure SQL Database, and Amazon Redshift. - Source: dev.to / about 3 years ago

What are some alternatives?

When comparing IBM Netezza and Azure Synapse Analytics, you can also consider the following products

Amazon Redshift - Learn about Amazon Redshift cloud data warehouse.

Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.

LibreOffice - Base - Base, database, database frontend, LibreOffice, ODF, Open Standards, SQL, ODBC

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Microsoft Office Access - Access is now much more than a way to create desktop databases. It’s an easy-to-use tool for quickly creating browser-based database applications.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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