In the rapidly evolving landscape of data management, choosing the right platform is critical for businesses aiming to harness their data effectively. Two prominent options, Microsoft Fabric and DataBoat, offer unique features and benefits tailored to different needs. This article provides a comparative analysis to help business decision-makers determine which platform best suits their requirements. Understanding the Platforms Microsoft Fabric: Fabric is an all-in-one data platform designed for modern enterprises. It seamlessly integrates data management, analysis, and application development, offering a unified framework for diverse data needs. DataBoat: DataBoat is an Analytics Platform powered by AI. It focuses on flexibility, cost-efficiency, and quick deployment, making it ideal for organisations looking for scalable and customizable solutions while avoiding vendor lock-in.

Intro With 15+ years of experience of working with data platforms, data warehouses and data lakes, together with many vendors and consultancy companies, the founders of DataOps House know the challenges most companies struggle with at both operational level as in the boardroom. Now we can say we solved the data challenge. At the heart of our innovative approach lies our all-in-one data platform, designed to revolutionise the way businesses deploy and manage their data environments. DataBoat offers an unbeatable combination of easy and fast deployment, exceptional scalability, and cost-efficiency. It gives the data analysts the power to understand the data, perform analysis and create valuable insights skipping data engineering tasks, once databoat can make the hard work. What sets DataBoat apart is its modular framework, which seamlessly integrates the best development applications for data ingestion with advanced operational capabilities. This unique blend not only facilitates a plug-and-play experience but also ensures that our solution can be tailored and scaled to meet the evolving needs of any organisation. Whether you're looking to streamline your operations or enhance your analytical capabilities, our adaptable and modular platform is your gateway to a parameterizable and scalable data solution. Join us as we explore how our data platform can transform your data strategy into a winning formula for success. Features DataBoat is a powerful platform designed to streamline and simplify your data management processes. With its robust connectors, DataBoat seamlessly brings data from point A to point B, effortlessly combining multiple data sources into a unified system. Its advanced scheduler automates data ingestion and transformation routines, ensuring that your data is always up-to-date and readily available. The platform boasts a versatile transformation module that caters to all your data manipulation needs. Whether you prefer using SQL language, a drag-and-drop interface, or leveraging the latest in Large Language Models (LLMs) to explore data using natural language/text, DataBoat has you covered. This cutting-edge capability allows users to interact with their data in the most intuitive and efficient way possible. Data Ingestion to your Data Landing Zone Gaining control over your data always starts with bringing all your data into one platform and having one single data truth. Although, doing this can bring a lot of complexity and working hours due to the differences in data sources and their implementation. DataBoat handles these complexities for you and offers an ever growing number of connectors that you can use out of the box to ingest your data simply by providing your credentials and pushing a single button and simple as that, your data has arrived to your landing zone. Data Transformation You ingested your data via DataBoat, or already have some data already on board is only the first step of the process because you need to make sure that data conforms to your needs. DataBoat provides a simple yet powerful editor where you can apply transformations on your data. It also lends a helping hand while doing so by providing a data catalog and LLM based AI assistance in writing your code. Data Governance DataBoat's Data Catalog is a sophisticated feature designed to empower users with a comprehensive and intuitive data discovery and management experience. This robust tool organises and indexes your entire data landscape, making it effortless to find, access, and understand your data assets. With powerful search capabilities and detailed metadata, users can quickly locate relevant datasets and gain valuable insights into their structure, lineage, and usage. LLM: large language models No modern application can exist without offering AI assistance while using it for the customers to make it more easy and intuitive to use. DataBoat is also a pioneer in this field. Its transformation module features query suggestions based on the user’s very own data catalog. Its LLM capability utilises the RAG technique for enhancing the accuracy and reliability of the generative AI model, which is also one of the most advanced available models. By using the best tools available the user only has to put a simple text based input on what they wish to see and get a specialised query for their use case, hence even non technical people are able to wield the skills of a data engineer. LLM example: a business user with no SQL knowledge urgently needs the numbers of last month’s shipments alongside with order ids but only after the 15th of May. The user can simply put for example “Please give me the shipments of last month with corresponding order ids after May 15th”. The LLM model creates a query out of this command like ChatGPT does.

Why do companies need a data platform? In today's data-driven world, a data platform is essential for companies looking to harness the full potential of their information. Data platforms streamline the integration, processing, and analysis of vast data sets, enabling businesses to make informed decisions quickly. By leveraging a data platform, companies can enhance operational efficiency, drive innovation, and gain a competitive edge in their respective industries. What is Fabric and what makes it special? Fabric is an all-in-one data platform designed to meet the needs of modern enterprises. It integrates data management, analysis, and application development into a single, cohesive, unique framework. This consolidation allows organisations to streamline their data operations, from ingestion to insights, eliminating the need for multiple disjointed tools and giving them a single source of data truth. Fabric provides a versatile platform that addresses the full spectrum of data management needs, from integration to analytics and everything in between the following rather impressive feature list: Data Integration: Fabric is your hub for data transformation and integration. With an ever growing number of connectors you can get data from anywhere and transform it exactly the way you want it. Data Engineering: Fabric supports robust data engineering tools designed to prepare and transform data for analytical readiness. This includes automation of data pipelines and scaling of data processing workflows, which are essential for maintaining data integrity and relevance. It also features Lakehouse concept which makes working with large amounts of data possible with great collaboration opportunities Data Warehousing: At the core of its architecture, Fabric incorporates a powerful data warehouse solution, optimising data storage for efficient querying and reporting. This centralised repository enables complex data analysis and supports the decision-making process. No matter how big your dataset is, Synapse Data Warehouse can handle it! Business Intelligence With PowerBI, the most used BI solution which feels for many users as “Excel on steroids” there is a big group of powerful stakeholders waiting to adopt this solution and turn data into insights. Real-Time Analytics: With Fabric, businesses can leverage real-time analytics to gain instantaneous insights from their data. Whether it's IoT data or logs, you get the insights you need, quickly and accurately. Data Science: Last but not least, Fabric has tools for the real data scientists. "Fabric extends its capabilities to data science, offering tools and environments that foster the development of predictive models and advanced analytics. Here you'll find everything you need to build, train, and deploy advanced AI models. OneLake: OneLake is Fabric's integrated data lake solution, designed to handle large volumes of diverse data in its native format. This is the central layer of Microsoft Fabric. This is where all your data is brought together and organised, so you can easily discover insights that would otherwise remain hidden. You also have the option to make this data available via separate data warehouses and lakehouses in different workspaces with specific security and policies. You get one OneLake per tenant, with data in different containers. Each OneLake can be split into multiple workspaces with their own access rules, so each team can manage its own data. Furthermore, you can host and explore all kinds of files with the different workload tools. Even more handy functionality is that you can use shortcuts to reference other storage locations. These shortcuts allow you to work with data without hosting it in Azure, reducing the risks of copying data. Unique feature: Warehouse VS Lakehouse Fabric adeptly bridges traditional data warehouse capabilities with the scalability of data lakes, forming a hybrid 'lakehouse' architecture. This integration offers the structured query capabilities of a data warehouse, combined with the vast data handling and machine learning readiness of a data lake. While both data warehouses and lakehouses serve as critical repositories for organisational data, they cater to different needs and use cases. A data warehouse is highly optimised for fast querying and streamlined reporting of structured data, primarily through SQL. It excels in scenarios where stability, data quality, and quick access to processed data are paramount. On the other hand, a lakehouse combines the robust querying capabilities of a data warehouse with the flexibility of a data lake. It is designed to handle not only structured but also semi-structured and unstructured data, supporting a wider variety of data formats like CSV, JSON, Parquet, and Delta. This makes the lakehouse ideal for more extensive data science and machine learning projects that benefit from large, varied datasets and require more complex data processing capabilities. Thus, the choice between a data warehouse and a lakehouse typically depends on the specific data strategies and analytical demands of the organisation. Fabric use case: NZA x Dataops House Challenge: New Zealand Auckland was a perfect, fertile ground for Fabric. The data of NZA comes from multiple sources, the processing of this data was handled mainly via non-automated, manual processes which is not only time consuming and prone to errors, but also declines scalability and having one single source of data truth.Because the business already adopted PowerBI, data literacy was on a medium level, they already use Microsoft as a partner and the amount of data limited to 40 stores and 1 webshop, Fabric stood out as the best choice. Solution: The implementation was a collaborative effort between DataOps House and NZA. Together, we strategically harnessed Microsoft Fabric, utilising its robust data pipelines and containerization capabilities. After careful planning, we started really from the ground up by creating the environment, we developed an innovative solution that automated the process of navigating the SFTP folder, ingesting data daily, applying transformations via Fabric DataFlows, centralising Fabric warehouse with automatically updated tables that are ready to be consumed and fuel dashboards, while taking an advantage of PowerBI capabilities of Fabric also. Result: guaranteed availability of complete and up-to-date PowerBI dashboard for daily decision making. Delivered components: Azure Landing Zone and MS Fabric Setup: Azure Landing Zone and MS Fabric Setup: NZA received comprehensive assistance in setting up their Azure subscription and configuring Microsoft Fabric within their environment. This included guidance on account provisioning, subscription management, and fine-tuning Microsoft Fabric to seamlessly integrate with NZA's existing infrastructure. Fabric Capacity Configuration: Additionally, DataOps House provided support in configuring the Fabric capacity, ensuring optimal performance and scalability for NZA's data operations. This involved fine-tuning the capacity settings to align with NZA's data processing requirements and future growth plans. DataOps House handled end-to-end data infrastructure setup Including pipeline development, workspace deployment, table creation, data modelling, and job scheduling

AI will change the world. MLops is trending. The hype of ChatGPT is big. Investments in IT and Data are massive. Yes, data can radically improve your business , but only if you know how to use it. So don't be put off by all the success stories of your competitors. In reality they probably also struggle with extracting value from their data, either because they lack the right tools, skills, or processes. That's where DataOps House comes in. Our mission and vision DataOps House is a data boutique consultancy with a mission; Help companies to democratize data for better decisions and operational excellence. We believe that data should be accessible, reliable, and actionable for everyone in the organization, not just a few experts. We want to be a trusted partner in your data journey, from strategy to implementation and support. We want to help you turn your ideas into products that create value for your customers and stakeholders. Our products and services We offer a range of products and services to help you achieve your data goals: Cloud & Data engineering Experts: We have a team of consultants who are the unicorns of the international market with experience in cloud and data technologies, such as AWS, Azure, GCP, Snowflake, Databricks, Kafka, Spark, Python, SQL, Airflow, and more. They can help you design, build, and optimize your cloud and data solutions. DataBoat: We have developed our own cloud data platform that is built on a winning combination of open source tooling, all selected for the right purpose. Our platform is scalable, secure, and easy to use and contains a cloud landing zone, data platform and MLOps platform. It allows you to ingest, transform, and democratize your data. You can also co-develop with us and don’t even need to change your cloud strategy. Data strategy: a vision and concrete roadmap how your data and data capabilities will enable and inspire your business strategy. Support: We provide support for your technology stack with the visibility and audibility necessary to guarantee your business continuity. We monitor your data pipelines, troubleshoot issues, and ensure quality and performance. Why choose us There are many reasons why you should work with DataOps House: Creating value: We help you execute your data strategy and turn your data into value. We can help you identify use cases, build data products, and measure outcomes. Nice professionals: We are nice people to work with and have extensive experience in various domains and industries. We are passionate about data and innovation. Sustainability: We help you minimize your carbon footprint by using efficient technologies. Lower TCO: We lower your total cost of ownership for storing, transforming, and democratizing data. We use open source tools that are cost-effective and flexible. Plug and play: With our winning combination of tools, we have solved the open source technology puzzle. You can easily integrate our platform with your existing systems and tools. Open and transparent: Our code is transparent and platform agnostic so you can co-develop with us How to get started If you are interested in working with DataOps House, you can contact us through our www.dataopshouse.com or email us at info@dataopshouse.com . We would love to discuss how we can help you make better decisions with data.