Microsoft Fabric vs Databricks vs Knowi
Which is the best unified data analytics solution among the three?
Table of Contents:
Introduction
Unified data analytics brings together big data and AI tools, helping enterprises quickly turn their data into insights that drive action. With unified data analytics, enterprises don’t have to pierce together different tools from multiple vendors. Microsoft Fabric vs Databricks vs Knowi are some of the popular unified data analytics platforms in the market today. This article is an in-depth review of these three platforms to help you know the components each has to offer for your enterprise.
Microsoft Fabric vs Databricks vs Knowi
Microsoft Fabric
Microsoft Fabric is an all-in-one analytics platform built for businesses and data professionals. It was launched in May 2023. It provides a unified platform for data engineering, data science, business intelligence, and machine learning. It is a cohesive platform that combines various tools and technologies into a single platform, including data lake, data integration, and data engineering, all in one place.
Fabric saves you from piecing together different services from multiple vendors. Instead, it lets you enjoy a highly integrated, end-to-end, and easy-to-use product that is designed to simplify your analytics needs. Fabric is built on top of Azure Data Factory and Azure Synapse Analytics.
Microsoft Fabric Features
Microsoft Fabric has an architecture that integrates a spectrum of essential components to form a comprehensive big data analytics platform. It offers the following features:
- Data Engineering
This component is the backbone of Fabric’s architecture, enabling users to collect, store, process, and analyze huge volumes of data. Users can create and maintain their data using a lakehouse, design pipelines for copying data into the Lakehouse, and use notebooks to write code (in languages such as R, Python, and Scala) for data ingestion, preparation, and transformation. It offers a lakehouse to help organizations store and manage both structured and unstructured data in a single location. They can then use tools and frameworks such as SQL-based queries and machine learning to process and analyze the data. Fabric users can define Apache Spark jobs, which are instructions on how to execute jobs on a Spark cluster and the transformations to apply to your Lakehouse data.
2. Data Factory
The Data Factory enables you to ingest data from a rich set of sources, offering features from both Power Query and Azure Data Factory. It has over 200 connectors to help you pull data from both on-premise and cloud storage.
3. Data Science
The Data Science component of Fabric enables users to build, deploy, and operationalize machine learning models within Fabric. It is integrated with Azure Machine Learning to facilitate built-in experiment tracking and model registry. Users can complete various activities across the entire data science process, from data preparation to exploration and cleansing to experimentation, modeling, and generating predictive insights and BI reports. Fabric gives users access to data science tools such as PySpark/Python, SparklyR/R, notebooks, and machine learning libraries such as Scikit-learn.
4. Data Warehouse
Microsoft Fabric has a lake-centric data warehouse that runs on an enterprise-grade distributed processing engine to provide SQL performance and scale. It separates compute from storage, allowing them to scale independently. The Fabric data warehouse is a combination of the worlds of lakehouses and warehouses offering a simple SaaS experience. It is tightly coupled with Power BI to facilitate easy analysis and reporting. They can use cross-database querying to leverage multiple data sources for zero data duplication and fast insights. Fabric users can create virtual data warehouses by creating shortcuts to their data regardless of where it is stored.
Data ingestion into the data warehouse is done using pipelines, cross-database querying, dataflows, or the COPY INTO command. The Fabric data warehouse has an editor that facilitates graphical data modeling and analysis to increase the speed to insights.
5. Real-time Analytics
Fabric supports Real-time analytics to give users quick access to data insights with just seconds of provisioning, indexing, automatic data streaming, and partitioning for any data format or source, and on-demand visualizations and query generation. It is optimized for time-series and streaming data. Real-time Analytics is fully integrated with all Fabric products for data loading, transformation, and advanced visualization scenarios. It uses a query language and engine for fast performance when searching through structured, semi-structured, and unstructured data.
6. Business Intelligence/Power BI
Power BI is a business intelligence tool. It helps Fabric users to access their data and use it to make better decisions. Power BI is administered by Microsoft Fabric. It is a service that can turn your data, whether stored in OneLake or Excel, into business insights to drive action. Power BI gives users access to several visualizations to help them present their data visually. You can also generate reports, dashboards, paginated reports, and semantic models from your data.
7. Pricing
Microsoft Fabric has two pricing models for Fabric Capacity usage, Pay-as-you-go and Reservation. The pay-as-you-go option has a minimum pricing of $0.36/hour for 2 Capacity Units (CUs) and a maximum price of $368.64/hour for 2048 CUs. The reserved pricing model is fit if you prefer to have predictable monthly costs for Fabric. It enables you to reserve resources and “lock” the price monthly for over one year.
However, the above prices are only for Fabric Capacity usage. You will additionally need to pay for OneLake storage, Mirroring, and Networking.
Benefits of Microsoft Fabric
Fabric users have the following benefits to enjoy:
- Unified Analytics
Microsoft Fabric integrates tools and technologies for performing various data analytics tasks into a single platform. This saves users from the need to pierce together different tools from multiple vendors.
2. Supports unstructured data
Microsoft Fabric offers a lakehouse where users can store and manage all types of data, including unstructured data.
3. Fit for advanced analytics
Fabric gives users access to tools for performing advanced data analytics such as building and deploying machine learning models.
4. Data Security
Fabric has robust security measures to keep your data and applications secure. Examples include identity and access management, threat detection, and data encryption.
Microsoft Fabric Limitations
Fabric is associated with the following limitations:
- Requires technical knowledge
Microsoft Fabric is only a suitable data analytics platform for users with technical skills. Technical knowledge is required to perform most tasks on the platform.
2. Complex Data Pipelines
Fabric’s Data Factory doesn’t have native connectors to all data sources. Users have to build data ingestion pipelines using ETL tools to pull data from some sources into the LakeHouse, which can be complex.
3. Poor Engineering Layer
Fabric requires users to lift and move their data into a centralized location, which is a cumbersome process. It also causes users to incur data storage costs.
4. Costly
Microsoft Fabric has different pricing plans for OneLake Storage, Mirroring, Networking, and Capacity Usage. It can be costly for an enterprise that wants to use all of these Fabric features.
Databricks
Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade analytics, data, and AI solutions at scale. It was founded in 2013. The platform integrates with cloud storage and security in your cloud account and deploys and manages cloud infrastructure on your behalf. Databricks uses Generative AI with a data lakehouse to understand your data. It automatically manages the infrastructure and optimizes the performance on your behalf. Databricks has Natural Language capabilities, allowing you to ask data questions in your own words. Natural Language assistance also helps you to troubleshoot errors, write code, and find answers in documentation. It is built on top of Apache Spark and provides features for data warehousing, data processing, and machine learning. Databricks is available on all major cloud providers, including Azure, AWS, and Google Cloud Platform. It brings big data and AI into a single platform, eliminating the need for disparate tools.
Databricks Features
Databricks offers the following features:
- Data Engineering
Databricks users can replace silos on one platform with a single and unified API for ingesting, transforming, and incrementally processing batch and streaming data at scale. It lets you use your preferred data engineering tools for data ingestion, ETL/ELT, and orchestration. Databricks users can leverage its ecosystem of technology partners to seamlessly integrate with data engineering tools. For example, you can ingest your business data with Fivetran, transform it with dbt, and orchestrate your pipelines with Apache Airflow.
2. Data Science
Databricks offers a unified and collaborative data science environment built on an open lakehouse foundation to streamline the end-to-end data science workflow. Users can write code in Python, R, SQL, and Scala, generate interactive visualizations, and uncover new insights with Databricks Notebooks. Databricks users can clean and catalog all their data (structured, unstructured,
batch, or streaming) in a single place with Delta Lake. It also provides visual tools for preparing, transforming, and analyzing data.
3. Data Warehouse
Databricks has Databricks SQL, a serverless data warehouse built on Lakehouse architecture that helps you run all your BI and ETL workloads. You can ingest data from anything like cloud storage to enterprise applications like Marketo, Salesforce, or Google Analytics using Fivetran. You can then use built-in ETL tools or your favorite tools such as dbt on Databricks SQL to transform your data. Analysts can use BI tools to uncover insights from their data. Databricks SQL enables users to query data and share insights using its built-in SQL editor, dashboards, and visualizations.
4. Artificial Intelligence
Databricks has the Mosaic AI feature that enables users to build, deploy, and monitor machine learning and AI solutions, from predictive models to the latest GenAI and large language models (LLMs). With Mosaic AI, organizations can securely and cost-effectively integrate their enterprise data into the AI lifecycle.
5. Real-time Analytics
Databricks simplifies data streaming to offer real-time analytics, applications, and machine learning on a single platform, helping enterprises benefit from real-time insights. You can use the tools and languages you already know to build streaming pipelines and applications faster, for example, SQL and Python.
6. Pricing
Databricks uses a pay-as-you-go pricing model in which pricing is determined by the products that you use at per-second granularity. Each component of Databricks has a pricing plan and the prices are quoted per Databricks Unit (DBU).
Benefits of Databricks
Databricks offers its users the following benefits:
- Unified Analytics
Databricks is a unified analytics platform, bringing together tools for data warehousing, analytics, and artificial intelligence into a single platform and eliminating the need for disparate tools.
2. Supports unstructured data
Databricks users can ingest data from various sources, including unstructured data sources, and clean and catalog it using its Delta Lake.
3. Fit for advanced analytics
Databricks has the Mosaic AI feature that enables users to build machine learning models for advanced data analytics.
4. Supports real-time analytics
Databricks supports real-time analytics, allowing enterprises to benefit from real-time insights and stay ahead of other enterprises.
Limitations of Databricks
The following are the limitations of Databricks:
- Requires technical knowledge
Databricks is only a suitable data analytics platform for users with technical knowledge. Most of the tasks require technical knowledge to accomplish. Users may have to write code in languages such as R and Python to perform some tasks.
2. Complex data pipelines
Databricks users have to build complex data pipelines for data ingestion, transformation, and orchestration using various data engineering tools.
3. Poor Engineering Layer
Databricks requires users to lift and shift data into a centralized location. This is a complex process that also causes users to incur storage costs.
4. Costly
Databricks has a different pricing plan for each of its components. It can be costly for an organization that wants to use most of its components.
Knowi
Knowi is a full-stack analytics platform that offers seamless integration across all data sources and empowers users with AI and search-based analytics. It lets users join data across both structured and unstructured data sources including NoSQL databases without requiring the data to be centralized. Knowi users can then analyze and visualize data across these databases with Knowi’s BI capabilities. They can also ask questions in plain English and use its Gen AI capabilities to generate insights from their data. Knowi doesn’t require users to use ETL/ELT steps to integrate and transform data, but they can effortlessly integrate data from different sources to analyze, visualize, share, and collaborate across an entire company.
Knowi Features
- Data-as-a-Service
Knowi has a Data-as-a-Service feature that enables it to natively integrate with a wide variety of data sources, including SQL, NoSQL, APIs, and files, without requiring users to rely on ETL or any third-party tools. Knowi users can blend data across multiple structured and unstructured sources. They can also use Cloud9QL, its powerful SQL-like syntax, to aggregate and manipulate data directly within Knowi without relying on additional data preparation tools. It has multiple levels of data security to ensure that your sensitive data is only exposed to authorized users.
2. Presentation Layer
Knowi’s DaaS solution lets you visualize your data on your favorite platform. It has a presentation layer that features over 30 visualization types and natural language capabilities sitting directly on top of its DaaS layer. You can use these visualizations to present insights and edit them to improve their look and feel.
3. Search-based Analytics
Knowi has a search-based analytics feature powered by Natural Language Processing to help both technical and non-technical users gain access to data easily. Users can type questions in plain English within Knowi and get back immediate results in the form of automatic charts and tables. It has the InstantSights feature that automatically highlights trends, key findings, and patterns to give you a summary of your data.
Knowi has also added its search-based analytics feature to Slack and Microsoft Teams, allowing users to ask questions directly from these apps and get instant results.
Users can also embed this feature into their own applications to allow their customers to ask questions in plain English directly from the applications and get instant answers.
4. Embedded Analytics
Knowi supports embedded analytics, allowing you to embed your dashboards, visualizations, and analytical experiences into the applications your team uses every day. It offers three embed options; simple URL embedding which uses a simple embed URL to let users view the embedded content without the need to log in, secure URL embedding which generates a secure embeddable URL using an encrypted hash key to keep the parameters encrypted, and prevent tampering, and JavaScript embed API that offers fine-grained control over embedded data visualizations, their appearance, and user access.
5. AI/Machine Learning
Knowi has AI/machine learning features to help business intelligence teams combine hindsight and foresight to trigger data-driven actions. With Knowi, you don’t have to use separate cumbersome data preparation tools to prepare your training data or move unstructured data into a relational structure. It has some built-in open-source algorithms, or you can upload your own proprietary algorithms, and they help you evaluate the best algorithm for your use case. It is easy to integrate your model into any analytics workflow.
6. Pricing
Knowi uses a custom-based pricing approach, in which pricing is determined by your needs. They have provided a form on their website that you can fill out to request a price quote. It offers three pricing plans namely Basic, Team, and Enterprise. Each plan comes with technical support, full onboarding, and everything else needed for success. It also offers discounts to non-profits and early-stage startups.
Benefits of Knowi
Knowi offers the following benefits:
- User-friendliness
Knowi is an easy-to-use data analytics platform. Knowi users can easily ingest data from any source without building data ingestion pipelines or following complex ETL steps.
2. Supports unstructured data
Knowi works well with unstructured data, without requiring users to transform or put it in a relational structure. It also supports native integration with NoSQL data sources, without requiring users to depend on third-party tools for data ingestion.
3. Enhanced data security
Knowi offers multiple levels of data security at the dashboard, visualization, or field level to ensure that sensitive data is only exposed to authorized users.
4. Fit for non-technical users
Knowi has a search-based analytics feature powered by Natural Language Processing that enables non-technical users to uncover insights from their data using plain English.
5. Less expensive
Knowi uses a custom-based pricing approach, in which pricing is determined by individual user needs, rather than pricing each of its components individually. Thus, it can be less expensive compared to other data analytics platforms. It also doesn’t require users to centralize their data, saving on storage costs.
Limitations of Knowi
Knowi users encounter the following challenges:
- Sophisticated user interface
Knowi gives business users access to a simple interface. However, its user interface for data engineers is a bit complex and may take some time to get familiar with.
2. Visualizations are not very beautiful
Knowi doesn’t have the “prettiest” out-of-the-box visualizations. However, its DaaS platform allows users to visualize data using their favorite platform. They can also create custom visualizations using CSSJavaScript to meet their unique needs.
3. Not open source
Knowi is a commercial tool.
Final Thoughts
Unified data analytics tools bring together big data, tools, and technologies, helping users quickly turn their raw data into insights. With unified data analytics tools, users don’t have to pierce together different tools from multiple vendors.
Microsoft Fabric, Databricks, and Knowi are some of the common unified data analytics platforms.
Microsoft Fabric is an all-in-one data analytics platform created by Microsoft. It requires users to centralize their data into its Lakehouse by building data ingestion pipelines. They can then transform and analyze the data by writing code in languages such as R and Python or generate BI reports using Power BI.
Databricks is a unified analytics platform that uses GenAI and a data Lakehouse to understand user data. Users can use their favorite data engineering tools to ingest data into its data warehouse and transform it. They can then write code in languages such as Python, R, and Scala or BI tools of choice to turn the data into insights.
Knowi is a full-stack analytics solution that offers direct access to all data sources, including NoSQL databases. Knowi enables users to join data on the fly, without requiring the data to be centralized or taken through ETL/ELT steps. Users can visualize their data using its optional presentation layer with 30+ customizable visualizations or using their favorite BI tools. Its search-based analytics feature helps users uncover insights from their data using plain English.
