Databricks

Our NextLytics experts have singled-out Databricks as the most feature-complete and most harmonic full-spectrum cloud-based Data and Analytics platform in the market. Our customers leverage Databricks highly efficient data processing and integration capabilities to gain insight into their business, to gain competitive advantage, and to produce measurable value from data.

Databricks combines state-of-the-art components and functions for data warehousing and data engineering in a single platform. Whether you are faced with large volumes of data, high velocity, unpredictable patterns, or frequently changing source schemas, Databricks offers the right tools to efficiently capture and harmonize your data. Building semantically rich, normalized data models for your warehouse becomes a breeze with efficient virtual layering on top of Delta Lake tables. It has never been easier to build a central, trusted data source for all your analytics needs—all on a single platform.

‣ Autoloader
Monitor changes to an object store or file system source and automatically process any changed or new files in real-time.

‣ Lakeflow ETL
Intuitive graphical user interface for building data loading pipelines for various source systems.

Databricks uses Apache Spark as its underlying distributed computing engine to process large amounts of data and demanding workloads. The platform automatically scales resources to match the scope of your tasks. Thanks to this consumption-based pricing model, you only pay for the computations you actually use. This flexibility allows teams to process large data sets, perform complex transformations, and conduct advanced analytics without worrying about capacity constraints or unexpected costs.

With Databricks, developers and data scientists can work together effectively in shared workspaces. The platform supports multiple programming languages, including Python, SQL, and R, making it the ideal environment for developing and maintaining data pipelines. Furthermore, built-in Git integration facilitates version control, encourages rigorous documentation practices, and enables teams to collaborate and track development progress in a transparent manner. Solutions can be developed, tested, and deployed directly on the platform via Jupyter Notebooks, allowing development teams to move seamlessly from prototyping to production without having to switch between different tools.

Databricks DevOps processes using Azure DevOps as an example: the Infrastructure-as-Code process is shown above the three Databricks system environments, which defines, checks, and deploys system components such as workspaces and authorization management, Spark clusters, Databricks jobs, etc. using Terraform. The development process for data processing in the Lakehouse is outlined in the lower half. SQL and Python Notebooks as well as central Python libraries are developed on the Databricks Dev-Workspace, versioned and synchronized with Git, automatically tested, and delivered via deployment pipeline.

With Databricks you can create a fully-virtual real-time data model across your logical layer through Delta Live Tables. Follow Data Vault or Medallion architecture patterns and use industry-standard collaborative modeling tools like dbt to define the expected outcome - instead of dealing with the technical batch processing logic. Once you have established the centrally governed data model in Unity Catalog, you can track performance and change history throughout the entire landscape with built-in Delta Table history and data lineage features. AI-powered, human-readable annotations in the Unity Catalog make it easier for business experts to navigate the data model and gain insight through the self-service SQL query interface and BI dashboards.

Databricks is a mature and constantly evolving development platform for ML, deep learning, generative AI, retrieval augmented generation, agentic AI systems and other variants of current data science trends. Like no other software product, Databricks combines AI and BI into a true data intelligence workspace - for fast insights and competitive advantage.

Databricks dashboards make it easy to monitor key performance metrics in real time without needing extra software or complex setup. By providing a range of visualization options, data teams can quickly create easy-to-understand reports. This allows you to identify trends faster, share insights directly, and make informed decisions for your business.

The process of developing machine learning models, preparing the input data, fine-tuning and evaluating, and finally hosting the best model version for production use, is generally referred to as MLOps. The Databricks ML platform allows you to train, deploy, and manage machine learning models along this process within an integrated environment that directly accesses and processes your data. This close link between data and modeling allows you to gain insights based on the latest information. Intelligent automation and predictive analytics open up new opportunities and optimize workflows. 

Instead of combining various different software products to fully support an end-to-end MLOps process, Databricks provides your Data Scientists with all the tools they need:

‣ Data access and feature engineering
Bring all your source data into Unity Catalog and use Jupyter Notebooks or Spark SQL to turn raw data into the optimized features for your ML model

‣ Feature Store
Fully defined features are stored in Unity Catalog using Delta Lake format and presented as dedicated Feature Tables for consumption in ML models

‣ Model Training
To develop and train new ML models, you can use the compute power of Apache Spark, any third-party Python libraries and machine learning frameworks like TensorFlow, XGBoost, Keras or Scikit-Learn. Keep track of versions, experiments, and performance during the development process with the fully integrated mlflow environment

Breaking new ground with agentic and generative AI

Databricks drives innovation through Agentic and Generative AI, providing access to LLM base models and AI integration within SQL queries. The Mosaic AI Agent Framework on Databricks is a comprehensive toolkit designed to facilitate the development, deployment, and evaluation of production-ready AI agents, such as Retrieval Augmented Generation (RAG) applications. This framework supports integration with third-party tools like LangChain and LlamaIndex, allowing developers to use their preferred frameworks while leveraging Databricks’ managed Unity Catalog and Agent Evaluation Framework.

With the Databricks Assistant, Databricks provides a powerful AI tool that leverages the power of large language models to assist users in a variety of ways using generative AI. This can be done by querying data in natural language, generating SQL or Python code, explaining complex code segments, or providing targeted help with troubleshooting.

The assistant which is based on the DatabricksIQ intelligence engine is deeply integrated in the Databricks platform and can provide personalized contextual responses regarding your data, code and parts of your configuration. More precisely, DatabricksIQ integrates information from your Unity Catalog, dashboards, notebooks, Data Pipelines and documentation to generate output fitting your business structure and workflows.

The SAP Business Data Cloud combines several previously independent products into a single full-spectrum cloud data and BI platform:

• Modern cloud data warehouse backed by HANA in-memory database
• Tightly integrated with classic SAP enterprise products like ERP, HR, or even SAP BW warehouses and BI solutions
• CitizenDeveloper Capabilities allow sharing data inside of an organization and empower business experts to build central data models
  
  

• Planning capabilities tightly coupled with data models from Datasphere or BW backend
  

• Mosaic AI and ML workspace
• Unity Catalog
• SQL Warehouse Query Engine
• development workspace & computing resources

At the beginning of 2025, a fully integrated Databricks workspace will become part of the SAP Business Data Cloud. Some of the core components of Databricks have been integrated into the SAP Business Data Cloud product suite:

• The Mosaic AI and ML workspace,
• The Unity Catalog of the lakehouse governance layer, and
• The SQL Warehouse query engine.
  
  

Also included are core Databricks features such as:

• Jupyter Notebooks as a development environment and

• Apache Spark and Photon Engine as scalable distributed computing resources.

SAP data models are seamlessly available in the Databricks catalog - and vice versa. This promises efficient, copy-free data integration between established SAP business domain data and third-party systems - or even unstructured data.

Technologically, this integration is based on Databricks' native data lakehouse innovations such as the Delta Lake table format and the Delta Sharing exchange protocol.

For companies that rely heavily on SAP and have already invested in the cloud-native Datasphere environment, SAP Databricks is a convenient solution. It offers an integrated alternative to a standalone Databricks solution.