Interest in Databricks has been on the rise over the past year. According to a TechCrunch study, Databricks’ market valuation has exceeded $43 billion, even as many other late-stage startups are experiencing a slowdown. The recent announcement of Salesforce and Databricks’ strategic partnership to bring Lakehouse data sharing and shared AI models has only raised the stakes. In this blog, we learn in-depth about Databricks and how it can yield tremendous business benefits.
How Can Databricks Help Your Business?
Databricks is a platform that unifies all your data, analytics, and AI workloads, facilitating seamless collaboration between various technical and business groups within an organization. It modernizes your data infrastructure, which is increasingly vital as traditional data architectures struggle to meet the evolving needs of companies. Databricks democratizes data across your organization, empowering employees to make smarter, data-driven decisions.
Databricks can also streamline your business processes through end-to-end automation. A product-driven mindset within an IT organization using Databricks ensures close collaboration with stakeholders, leading to the development of tools and systems that improve business outcomes.
Another way Databricks can drive business is by managing vast amounts of data from various sources. It provides a single platform for storing, cleaning, and visualizing data, which can significantly improve your data management processes. Databricks addresses the challenges of data growth and integration, leveraging the benefits of multi-cloud strategies and open-source technology.
Databricks Brings the Best of Both Worlds - Data Lakes and Warehouses
The Databricks Lakehouse platform incorporates optimal features of data lakes and data warehouses. Data lakes support diverse open formats, facilitate machine learning, and efficiently process and store data at a lower cost. However, due to underwhelming transaction support, they fall short in Business Intelligence (BI) reporting. Conversely, while data warehouses are excellent for BI reporting, they have limited support for unstructured data, data science, AI, and streaming. Their closed proprietary formats are also expensive to scale.
The Data Lakehouse platform is a multi-faceted solution capable of supporting a range of roles and diverse workloads. Utilizing the ‘Delta Lake’ format, the platform incorporates ACID transaction support, eliminating the need for repeated data processing by only updating the altered data. Delta Lake represents an improvement over the traditional ‘data lake.’ It enhances control and reliability by maintaining multiple data versions while employing vacuum operations to merge and clear out old versions, optimizing storage.
With remarkable data processing and indexing speed, Delta Lakes is 48 times faster than other competing big data technologies. The Lakehouse platform is distinctive, with features such as schema enforcement, governance support, open format compatibility, and time travel capabilities. Databricks Delta Lake skillfully bridges the gap between the benefits of data lakes and data warehouses, making it the best of both worlds.
Databricks and Data Governance: A Powerful Combination
Databricks provides a service called Unity Catalog that addresses multiple functional areas under the umbrella of data governance, as summarized in Table 1.
| Area | Description |
|---|---|
| Data Access Control | Unity Catalog maintains a repository of all data items for governance. |
| Data Access Audit | Unity Catalog captures access to all data in the system, including who has access within the organization. |
| Data Lineage | Unity Catalog discovers the upstream and downstream consumers and how data is getting transformed. |
Unity Catalog addresses the governance functional areas summarized in Table 1 using common table control access lists across the cloud platforms. The Catalog can be shared across multiple workspaces.
Databricks Compute Resource Management
A cloud ‘compute’ resource pertains to the on-demand availability of computer system resources, particularly data storage and computing power, without direct user management involvement. It refers to infrastructure elements encompassing hardware and software that facilitate problem-solving and solution development by receiving and analyzing data.
Databricks orchestrates compute resources to ensure optimal performance and efficiency. Databricks compute management is characterized by its automatic isolation of tasks, preventing any task from interfering with another. This isolation ensures a dedicated and uninterrupted environment for each task, enhancing overall productivity. Databricks provides the convenience of managing all these compute resources under a unified platform, simplifying the process and saving time.
Databricks provides two types of clusters to process data loads in the system:
- All Purpose Clusters: analyze data collaboratively using notebooks and can be created through APIs or from a workspace.
- Job Clusters: automatically initiates at the start of a job and terminates when the job ends.
Databricks Streaming Support
The combination of structured streaming and Delta Lake simplifies and optimizes incremental extract, transform, and load (ETL) processes. The 'spark.readstream' and ‘spark.writestream’ APIs allow the streaming process to operate similarly to batch processing for incremental modifications. The ‘Trigger.AvailableNow’ mode, akin to ‘Trigger.Once,’ can execute numerous batches until all available data is processed instead of one large batch. This feature was introduced in Spark 3.3.0 and Databricks runtime 10.4 LTS.
Delta Lake is an ideal solution for seamlessly tracking and propagating inserted data across a sequence of tables such as raw, curated, integrated, target, and others. Furthermore, the MERGE INTO directive in Databricks SQL supports efficient pipeline development using change data capture.
Accidental Data Deletion? Databricks to the Rescue!
Delta tables in Databricks solve the problem of accidental data deletion. Delta tables maintain multiple versions of the data. You can easily retrieve older versions of the data with simple SQL statements. For instance, the ‘UNDROP’ command allows the recovery of accidentally dropped or deleted tables from the Unity Catalog within a 7-day retention period.
Delta Lake tables create a new table version for each operation that modifies them. The history of these operations can be retrieved using the 'history' command. This version history ability facilitates auditing, rollback, or querying the table at a specific point in time, providing a safety net against accidental data loss or modification.
Additionally, Delta Lake manages the automatic removal of log files after a table has been checkpointed. This feature reduces the risk of accidental data file deletion when running the ‘VACUUM’ command against a table. However, it's worth noting that while these features provide safeguards, they are not meant to serve as a long-term backup solution.
Databricks Functions That Help Organize Data
Let’s check out detailed guidance on specific Databricks functions that can help you with data organization.
Higher-order functions in Spark SQL allow you to work directly with complex data types. Records are frequently stored as array or map-type objects when working with hierarchical data. Higher-order functions will enable you to transform data while preserving the original structure of data. For example, Spark SQL has a ‘schema_of_json’ function to derive the JSON schema from an example JSON string.
Aggregating Unique Values
The ‘collect_set’ function can collect unique values for a field, including fields within arrays. It facilitates the aggregation of unique values into a single collection within a group. This function provides a streamlined approach to data management and manipulation, allowing you to easily group and analyze disparate data points.
When dealing with large datasets, the ‘collect_set’ function can greatly simplify the task of identifying unique values. Additionally, when used with the ‘DISTINCT’ command, the function can ensure the collection of unique values, effectively serving as a synonym for itself.
Data Structure Simplification
The ‘flatten’ function can combine multiple arrays into a single array. This lets you transform complex, nested data structures into a simpler, flat format. It is advantageous when dealing with data stored in arrays or other nested structures, as it simplifies the process of data analysis and manipulation. By converting the nested data into a single-level, linear format, 'flatten' makes it easier to perform operations like searching, sorting, and filtering. It also aids in better data visualization, as flat data structures are generally more straightforward to represent graphically.
Extracting Unique Values
The ‘array_distinct’ function removes duplicate elements from an array, helping you to streamline data analysis and manipulation processes. This function takes an array as an input and returns a new array with distinct elements in the original array. It eliminates any duplicate entries within the array, ensuring only one instance of each value. This is particularly useful when analyzing data sets with unique values or cleaning data for further processing.
Our Success Story: Databricks Offers Quality KPI Data
A major footwear retailer has thousands of stores and outlets worldwide. They experienced difficulty ingesting data from diverse sources and lacked a clear picture of key performance indicators (KPI). The lack of timely information meant the company was unable to make accurate inventory predictions, which would result in lost sales and revenue opportunities. Read this case study to learn how our Data Analysts used Databricks to provide timely and accurate data to the company’s o9 data analytics platform.
If you wish to maximize the value of your data using Databricks, download this white paper for a step-by-step process: “Maximize the Value of Your Data: Managed Spark with Databricks vs. Spark with EMR vs. Databricks Notebook.”
Conclusion
Databricks is an excellent choice for data governance because it provides robust, efficient, and scalable solutions for data management, processing, and analysis. This is achieved through features like Unity Catalog, a platform governance solution that addresses critical data governance issues, streamlines processes, and increases data awareness. The platform's security measures and data classification systems enhance privacy and compliance. Additionally, Databricks provides unique functions for data handling like ‘collect_set,’ ‘flatten,’ and ‘array_distinct,’ which simplify data management and refinement for accurate analysis. This, in turn, leads to improved productivity, faster decision-making, and enhanced collaboration.
Solid data governance is vital to leveraging your data assets, earning customer trust, and gaining a competitive edge. With proper governance, your business can avoid challenges with data security, privacy, regulatory compliance, and quality management. You might also miss out on the benefits of operational efficiency, cost reduction, and value realization that come with good data governance. Adopting a tool like Databricks that ensures strong data governance can help your business stay ahead in today's data-driven world.
Rama Vemula
Information Analytics
Published Feb 12 2024
