Microsoft Fabric: A Comprehensive Data Management Solution Microsoft Fabric presents a unified, robust platform designed to optimize data management, enhance AI model development, and empower users across an organization. It focuses on integrating data seamlessly, ensuring governance and security, and providing AI capabilities. Microsoft Fabric is presented as an all-encompassing data management solution, providing organizations with tools for efficient data integration,…
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OpenText Vertica
Score 10.0 out of 10
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The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by OpenText.
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Pricing
Microsoft Fabric
OpenText Vertica
Editions & Modules
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Offerings
Pricing Offerings
Microsoft Fabric
OpenText Vertica
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
Use Microsoft Fabric by purchasing Fabric Capacity, a billing unit that enables each Fabric experience. Pay for every data tool in one transparent, simplified pricing model and save time for other business needs.
Fabric Capacity is priced uniquely across regions.
I would highly recommend Microsoft Fabric, especially for medium to large enterprises aiming to build a robust, scalable, and secure data analytics platform. It effectively unifies various data workloads, streamlining data integration, engineering, and particularly enhancing our ability to create and share reliable Power BI dashboards. The deep integration with Azure AD for features like Row-Level Security is a significant advantage for data governance.
Vertica as a data warehouse to deliver analytics in-house and even to your client base on scale is not rivaled anywhere in the market. Frankly, in my experience it is not even close to equaled. Because it is such a powerful data warehouse, some people attempt to use it as a transactional database. It certainly is not one of those. Individual row inserts are slow and do not perform well. Deletes are a whole other story. RDBMS it is definitely not. OLAP it rocks.
Could use some work on better integrating with cloud providers and open source technologies. For AWS you will find an AMI in the marketplace and recently a connector for loading data from S3 directly was created. With last release, integration with Kafka was added that can help.
Managing large workloads (concurrent queries) is a bit challenging.
Having a way to provide an estimate on the duration for currently executing queries / etc. can be helpful. Vertica provides some counters for the query execution engine that are helpful but some may find confusing.
Unloading data over JDBC is very slow. We've had to come up with alternatives based on vsql, etc. Not a very clean, official on how to unload data.
I've rated Microsoft Fabric's overall usability as a 4, primarily due to its extensive and multifaceted feature set, which can make it challenging to navigate and determine the optimal functionality for a given task.While the breadth of capabilities is a core strength for large enterprises, it often leads to a sense of being "lost" or overwhelmed for teams like ours that do not have highly formalized roles or dedicated specialists for each Fabric "experience" (e.g., Data Engineering, Data Warehousing, Data Science).
I haven't had any recent opportunity to reach out to Vertica support. From what I remember, I believe whenever I reached out to them the experience was smooth.
Microsoft Fabric integrates data ingestion, engineering, warehousing, and Power BI visualization into one cohesive environment. This "one-stop shop" approach dramatically reduces complexity, minimizes operational overhead, and eliminates the need to integrate disparate tools and manage data across multiple systems. It provides superior scalability for large datasets, supports open data formats, and offers a much broader suite of data engineering and data science capabilities.In essence, Fabric's integrated ecosystem and streamlined operational management were key differentiators, providing a more cohesive, scalable, and efficient solution for our evolving data strategy than combining specialized tools.
Vertica performs well when the query has good stats and is tuned well. Options for GUI clients are ugly and outdated. IO optimized: it's a columnar store with no indexing structures to maintain like traditional databases. The indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process.