As the snow melts, it's time to build a data skyscraper with Databricks
Introduction
The Databricks platform is powerful and comprehensive and — because of the breadth of capabilities for analytics, data science, genAI and the wide range of personas that could use the platform — there is inherent complexity in setting up the right infrastructure and the data pipelines. It’s very well designed to meet all of a company's data needs, offering a simpler solution than its predecessors, such as Hadoop, and a more complete offering than competitors like Snowflake.
Funny or not, building a secured, governed and scalable data platform that supports multiple types of use cases along with the data management processes and practices is very similar to building a skyscraper - the higher the building grows and supports more units and people, the complexity increases.
This guide will help you understand the complexities of Databricks, ensuring your data skyscraper stands tall and proud.
Imagine constructing a state-of-the-art skyscraper in the city of Datanopolis, symbolizing your business's aspirations and potential. Now think of Databricks as the most advanced & versatile building material available, offering the strength and flexibility needed to not just erect a structure but to create a landmark.
People will be amazed by this building, as it features dazzling elements like panoramic observatories (serving as Analytics & BI) and specialized spaces (ML & GenAI functions). However, as with any construction project of such magnitude, seasoned architects and construction teams (like SunnyData's consultants) become indispensable.
What's more, a skyscraper requires a robust foundation (Data Engineering) that assures the structure is secure. Without expertly laid data pipelines and a solid engineering base, the most advanced Data Analytics & AI ambitions will falter before reaching their full potential. dddd
More things in common that one would guess
Building Materials > Databricks enables a range of capabilities from data ingestion, data engineering to supporting Analytics, ML & AI.
Foundation > Data Engineering ensures robust, scalable architecture that is repeatable and scalable for all subsequent use cases.
Specialized Specs > GenAI Functions offer features that provide cutting-edge insights and innovations.
Infrastructure (eg.: Plumbing) > DevOps integrates automates processes, ensuring seamless data flow and operation.
Security > Data Security & Governance, and ensuring compliance with Unity Catalog - or complement with other enterprise data catalogs.
Interior Design & Furniture > Analytics & BI transforms data into actionable insights, making it accessible to business users and natural language ability to ask questions of your data.
Architect & Construction Crew > Like SunnyData's Consultants, Databricks experts tailor and implement solutions for specific business needs.
Repeatable patterns in every project
The Basement serves as the Discovery phase: It's crucial groundwork that sets the direction of the structure. We suggest addressing 3 critical aspects to assure scalable growth:
Start by mapping out the project's goals, needs, and timelines to align with the big picture (a.k.a. Functional Requirements Analysis). Next, evaluate the necessary infrastructure and tools, ensuring Databricks setups are optimized (a.k.a. Technical Requirements Analysis). Finally, assess and enhance data governance maturity, focusing on clear policies, roles, and management commitment for a solid foundation (a.k.a. Data Governance Maturity Analysis).
Configuration starts in the ground level: It's here that the foundation laid in the discovery phase begins to take shape, evolving into a structure capable of supporting the ambitious designs of data analytics and AI applications to come.
Security: Implementing robust security measures such as Single Sign-On, access control lists, credential passthrough, and VNET injection to ensure the integrity and confidentiality of the data environment.
Administration & Setup: Tailoring cluster policies, configuring additional libraries, employing initialization scripts, and extending Docker images for complex adaptations, such as web scraping functionalities.
Infrastructure Components Configuration: Setting up Databricks clusters, scaling policies, assigning clusters based on application and team roles, optimizing caching, and managing data catalogs within Databricks.
Data Engineering is the key to successful future applications: It’s crucial for building future advanced applications. Period. This phase is where the robust architecture of data management is established, encompassing critical tasks such as data movement (capture, replication, migration) and the development and optimization of ETL processes.
Applications, from dashboards and predictive models to deep learning and artificial intelligence, are the practical manifestations of this foundational work.
The approach to building this data infrastructure can vary. A phased, use case-driven strategy is recommended over a 'big bang' method, allowing for iterative integration and refinement of data sources and applications. This method ensures that the data architecture is not only scalable but also adaptable to evolving business needs, setting the stage for future expansions and innovations.
Our Tip: Start Small, Dream Big
Don't try to build every floor at once. Begin with a few key data projects that have clear business value. These early successes will build team confidence and inside your organization, justifying further investments.
By embracing a structured approach with Databricks, businesses unlock a new realm of possibilities. Take, for example, a regional US Bank we helped leading to a 400% reduction in TCO overall data analytics infrastructure, operations and employee costs.
This success story underscores how, from the ground up, our methodology not only strengthens operational frameworks but also paves the way for groundbreaking innovations, turning data into a lever for market differentiation and competitive advantage.
The foundational work we did enabled pivotal applications, enhancing operational efficiency and strategic insight across the business spectrum. Some examples:
Data Platform Decommissioning: Streamlining data ecosystems by phasing out legacy systems.
Inventory Consolidation: Integrating disparate stock systems to provide a unified inventory view, enhancing decision-making.
Unified Sales View: Aggregating sales data from multiple channels for a holistic sales analysis.
Logistics Optimization: Leveraging data to streamline supply chain processes, reduce costs, and improve delivery times.
Data Enrichment: Enhancing existing datasets with additional external or internal data sources to provide deeper insights.
Customer Segmentation: Utilizing data to categorize customers into distinct groups for targeted marketing and improved customer service.
The Foundation Model APIs enable you to:
Develop LLM applications for either development or production environments, supported by a scalable and SLA-backed LLM serving solution capable of handling spikes in production traffic.
Efficiently evaluate different LLMs to determine the most appropriate choice for your specific needs, or to replace a currently deployed model with one that offers superior performance.
Transition to open-source model alternatives from proprietary ones to enhance performance while reducing costs.
Utilize a foundational model in combination with a vector database to develop a chatbot that employs retrieval augmented generation (RAG).
Use a general LLM for creating an immediate proof-of-concept for LLM-based applications before the commitment of resources to train and deploy a custom model.
Apply a general LLM to confirm the viability of a project prior to allocating additional resources.
Our Conclusion
While people seek for a beautiful penthouse with the views, that come with nice amenities, with security and located in a great neighborhood for them with ease of access to groceries and transport; Enterprises look for positive outcomes such as customer retention, net promoter score, increased wallet share, costs reduction, new & better products, self service, faster and timely insights, accurate reporting of customer data, among others.
Don’t get overwhelmed by all that. By constructing your project piece by piece, with a data engineering foundation and making sure every layer is secure and compliant, your data skyscraper will stand out in the skyline.
Plus, with SunnyData as your architect and construction partner, you're equipped with the expertise and insight needed to ensure your project not only reaches its ambitious heights but also serves as a beacon of innovation and efficiency in the data world making you proud.
