In some sense, every company is a data company. You might make cars, sell software or manage financial assets, but the moment you have to make decision based on real information, you need to rely on data engineering.
Data engineering is the work of collecting raw data, cleaning it and processing it to ready for people to use. That means we can feed a report for your CFO or trigger a real-time alert when a machine on a factory floor is about to fail.
The problem is that doing this well is very hard. Because data is often spread across multiple systems, different teams use various tools at each step. So, when pipelines break down, users have to wait days or weeks for answers that could only take minutes. However, Databricks solves this problem by offering an all-in-one platform for data engineering. It streamlines data ingestion, transformation, orchestration, governance, and AI readiness. So, more than 20,000 organizations, including 60% of the Fortune 500, rely on Databricks to unify their data operations.
In this article will explore how Databricks works with data engineering, the challenges it addresses, and how businesses can use it to build data infrastructure that truly supports their goals.
Market Challenges
To fully understand what databricks offers, it's important to first look at the challenges that data teams face today. These problems are real and affect companies every day.
The Data Volume Problem
By the end of 2026, global data is expected to reach 181 zettabytes, almost double in last 2 years. Every day, 2.5 quintillion bytes of data are created. This overwhelming volume is too much for many traditional systems to handle, leading to performance issues and higher costs. So, when data systems get overloaded, queries take longer and analysts have to wait for results.
Tool Sprawl
Many CTOs face a common issue because of too many tools for different tasks. One tool for ingesting data, another for transforming it, and yet another for running analytics. Over time, companies end up with 8-12 different tools that don’t work well together. The result is engineers spending more time managing these tools and integrations than actually solving problems. A 2024 survey found that 38% of data teams struggle with this “tool sprawl,” and 45% see integration complexity as their top challenge.
Data Silos
In most companies, data is stuck in separate systems. Sales data might be in Salesforce, HR data in Workday, and finance data in a completely different system. These systems were built at different times by different vendors, so sharing data between them is a challenge. According to MuleSoft, the average company runs 897 applications, but only 29% of them are integrated. This means a lot of valuable data is trapped in silos, making it harder for analysts and AI models to access it.
Real-Time Demands
Businesses now need real-time data to keep up with customer expectations, supply chains, and fraud detection. However, building real-time data systems with traditional tools is both expensive and fragile. So, many companies end up with separate systems for batch processing and real-time streaming, which only adds to the complexity.
Governance and Compliance
With regulations like GDPR, HIPAA, and PCI-DSS, companies need full visibility over their data like who can access it, where it is, and what’s happened to it. However, managing this visibility across a fragmented toolset is difficult, leading to compliance gaps. Bad data governance can cost companies millions every year due to poor decisions and operational errors.
These challenges are common across industries, and they highlight why having a powerful platform like Databricks can make all the difference.
Databricks Overview
Databricks was founded in 2013 by the team who created Apache Spark at UC Berkeley. The company started with a mission to make big data processing easier but over the decade, it has grown into something much bigger: a unified platform for data, analytics, and AI.
The core idea behind Databricks is the Lakehouse. A traditional data warehouse is great for structured SQL analytics but struggles with unstructured data and AI workloads. A data lake is great for storing any kind of data cheaply but makes governance and analytics hard. The Lakehouse combines both. You get reliable, governed, structured access to data sitting in open-format cloud storage.
How big is Databricks today?
| Metric | Number |
|---|---|
| Customers worldwide | 20,000+ organizations |
| Fortune 500 customers | 60%+ |
| Company valuation (2026) | $134 billion |
| Cloud platforms supported | AWS, Azure, Google Cloud |
| Notable customers | Porsche, Volvo, Hinge Health, Corning, Shell |
What makes Databricks different?
Most data platforms were built to solve one piece of the puzzle. But databricks was designed from the start to unify the entire data lifecycle. The platform runs on open standards (Delta Lake, Apache Iceberg, Apache Spark), so your data is never locked into a proprietary format. You own your storage. And, you can take your data anywhere.
The platform introduced AI assistance as tools, it helps engineers to find, build, and monitor pipelines using natural language. It’s not for marketing team, this works directly with workflow development without spending time to do repetitive tasks.
Key Capabilities
Databricks organizes its data engineering capabilities under a product called Lakeflow. Lakeflow covers the three things every data engineering team needs: getting data in, transforming it, and scheduling it to run reliably in production.
Lakeflow Connect (Data Ingestion)
Lakeflow Connect simplifies the process of bringing data into your Databricks environment. While this may sound easy, it's often one of the most time-consuming tasks in data engineering. Custom connectors can fail, source systems change, and sudden spikes in data volume can cause issues.
Lakeflow Connect offers reliable, scalable connectors for the most commonly used systems.
| Source Type | Examples |
|---|---|
| Enterprise Applications | Salesforce, Workday, ServiceNow, SharePoint, NetSuite, Google Analytics |
| Databases | MySQL, PostgreSQL, SQL Server, Oracle, Amazon RDS |
| Streaming Sources | Apache Kafka, event streams, IoT devices |
| File Sources | Amazon S3, Azure Data Lake, Google Cloud Storage |
| Data Warehouses | Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse |
The connectors use change data capture (CDC), which means they only pull new or updated records instead of reloading everything from scratch each time. This makes ingestion fast, cheap, and easy on source systems.
Lakeflow Declarative Pipelines (Transformation)
Once data is in the platform, you need to clean it, join it, and shape it into something useful. Lakeflow Declarative Pipelines handles this. Because It was previously known as Delta Live Tables (DLT), and it has been upgraded into a full declarative pipeline framework.
The key word here is declarative. So, Instead of writing code that tells the system exactly how to run a job step by step, you write code that describes what the output should look like. Databricks figures out the execution plan, handles incremental processing, manages retries, and scales compute automatically.
You write your transformation logic in SQL or Python, and the platform handles the hard operational parts:
- Automatic incremental processing
- Real-time mode for low-latency pipelines
- Built-in data quality checks
- Automatic compute scaling
- A visual IDE that shows your pipeline as a diagram alongside your code
The pipelines follow what Databricks calls the Medallion Architecture, which is a widely adopted pattern for organizing data into three layers:
| Layer | What It Contains | Purpose |
|---|---|---|
| Bronze | Raw data as it arrives from source systems | Keep everything, nothing is lost |
| Silver | Cleaned, filtered, and joined data | Consistent, trustworthy records |
| Gold | Aggregated, business-ready data | Ready for reports, dashboards, AI |
Lakeflow Jobs (Orchestration)
Building pipelines is one thing, Running reliably in production on a schedule, with proper error handling and monitoring, is another. However, Lakeflow jobs handles orchestrations across all workload types like ingestion pipelines, transformation pipelines, SQL queries, notebooks, machine learning training, and model deployment.
Key features includes
- Trigger based scheduling
- Advanced control flow with branching and conditional logic
- Real-time data triggers
- CI/CD integration for deploying pipeline changes safely
- Alerts through PagerDuty, Slack, and email
- Full data lineage so you can trace every record from source to dashboard
Delta Lake (Storage Layer)
Delta Lake is the open-source storage layer that sits underneath everything in Databricks. It brings database-style reliability to your data lake. Without Delta Lake, a data lake is essentially just a pile of files. With it, you get:
- ACID transaction
- Time travel
- Schema enforcement
- Automatic small file compaction
- Scalable meta data handling for tables with billons of rows
Delta Lake is an open standard, which means other tools like Apache Spark, Flink, and even Snowflake can read Delta tables directly. Basically, your data is not locked in.
Unity Catalog (Governance)
Unity catalog is single place to manage access, lineage, and data quality across every asset in your platform like tables, models, dashboards, files, and metrics.
Before unity catalog, companies running multiple databricks workspaces to manage security separately. But after Unity Catalog, it change account-level governance that applies across all workspaces and all clouds.
Key features:
- Row and column-level access control
- Automatic PII detection and tagging
- End-to-end column-level data lineage
- Attribute-based access control (ABAC) for dynamic, tag-driven policies
INFO:
Unity Catalog is now open source. Databricks made this move to ensure data teams are never locked into a single governance layer and can work across tools and engines freely.
Databricks SQL and Photon Engine
Databricks SQL gives analysts and business users a familiar SQL interface to query data in the Lakehouse. It runs on serverless compute, so there no clusters to manage. The Photon Engine, a vectorized C++ query engine built into the platform, it significantly accelerates query performance for large-scale analytics workloads.
The result is that business analysts can run complex queries on billions of rows and get results in seconds, without needing to understand the infrastructure running underneath.
Business Benefits
Technology benefits matter, but business leaders care about outcomes. Here is what companies actually gain when they consolidate their data engineering on Databricks.
Lower total cost of ownership
Running 8 to 12 separate tools for data engineering is expensive. You pay licensing costs for each tool, engineering time, and operational overhead to keep everything running together. However, consolidating one into single platform removes most of the costs.
Companies using the Medallion Architecture with Databricks report significant savings from better compute efficiency. Also, Incremental processing means you only compute what changed, not everything, which cuts cloud spending directly.
Faster time to insight
With fragmented data infrastructure, moving a new data product from idea to production can take weeks or even months, as teams rely on each other and pipelines must be rebuilt across various tools. A unified platform speeds up the process, reducing the time to just days.
For example, Hinge Health managed 10x data growth while keeping costs in check by consolidating their data pipelines on Databricks. Volvo built a real-time inventory management system for hundreds of thousands of spare parts globally. Corning automated repeatable data workflows for multiple teams, moving data through the Medallion Architecture with minimal manual effort.
AI-ready infrastructure
You cannot build reliable AI without reliable data. This is the most important lesson companies learn when they try to deploy AI at scale. So, the data engineering foundation needs to be solid before AI can deliver results.
Databricks was designed with AI at its core. It lets you easily connect your data pipelines to ML training, model serving, and AI workflows. Unity Catalog helps manage both your data and AI models, while MLflow 3.0 tracks your experiments and handles model deployment. This all-in-one platform makes it simple to go from raw data to a fully deployed AI model.
Research by McKinsey estimates AI could add $13 trillion in economic value by 2030, but only for companies with the data infrastructure to support it. Databricks is built for that scenario.
No vendor lock-in
All core Databricks storage uses open formats like Delta Lake and Apache Iceberg. Your data sits in your own cloud storage (S3, Azure Data Lake, GCS), Meanwhile, you can read data with any tool that supports these formats.
This matters especially for large enterprises that run multiple cloud environments or need to share data with partners on different platforms.
Implementation Guide
Getting started with databricks does not required big bang migration. The most successful implementations start small, prove value quickly, and expand from there. Let’s see the practical path of how most organizations works.
Phase 1: Assess and plan (Week 1-2)
Before touching any technology, understand what you have and what you need.
- List your current data sources and how data moves between them today
- Identify your biggest pain points
- Pick one or two high-value use cases to start with, not everything at once
- Decide which cloud platform you choose for deploy (AWS, Azure, or Google Cloud)
Phase 2: Set up your workspace (Week 2-3)
Databricks runs in your cloud account, not Databricks infrastructure. You create a workspace in your cloud environment and connect it to your data sources.
- Create your Databricks workspace through AWS, Azure, or Google Cloud marketplace
- Set up Unity Catalog from day one
- Configure your cloud storage (S3, ADLS, or GCS) as your primary storage layer
- Set up networking and security per your company policies
- Create user groups and initial access policies in Unity Catalog
Phase 3: Ingest your first data source (Week 3-4)
Pick your most important data source and connect it with Lakeflow connect.
- Configure the connector using the no-code UI or the API
- Run the initial load into your Bronze layer in Delta Lake
- Verify data completeness and confirm CDC is working correctly
- Set up basic monitoring and alerting for the ingestion pipeline
Phase 4: Build your Medallion Architecture (Week 4-8)
Once data is in Bronze, build out your Silver and Gold layers.
- Silver: Write transformation logic in SQL or Python using Lakeflow Declarative Pipelines to clean and standardize raw data
- Gold: Build aggregated, business-ready tables for specific use cases (reports, dashboards, ML features)
- Apply data quality checks at each layer so bad data is caught early
- Register all tables in Unity Catalog with ownership and access policies
Phase 5: Operationalize (Week 8-12)
Make your pipelines production-ready.
- Set up Lakeflow Jobs to schedule and orchestrate your pipelines
- Connect CI/CD so pipeline changes go through version control and testing before deployment
- Set up alerting for pipeline failures, data quality issues, and freshness SLAs
- Build dashboards in Databricks SQL for your first set of business users
- Run a knowledge transfer session so your team can maintain and extend the setup
Phase 6: Expand
Once the first use case is running well, bring in more data sources, more business teams, and more use cases. The platform scales without requiring you to redesign the architecture. The governance model you set up in Unity Catalog applies automatically as you add more data and more users.
Conclusion
Today, data is the heart of every business decision. From raw data collection to its final use in reports or real-time alerts, data engineering is the backbone of businesses to make decisions quickly. However, managing data across multiple systems and tools can often lead to delays and inefficiencies.
As business continue to scale, having a reliable and efficient data infrastructure is crucial for supporting growth and achieving strategic goals. Meanwhile, databricks all-in-one platform has proven to be a game-changer for over 20,000 organizations, including 60% of the Fortune 500, simplifying data engineering workflows and accelerating decision-making.
At Lucent Innovation, we specialize in building robust data engineering services with Databricks. Whether you're looking to unify your data operations, optimize your pipelines, or leverage AI capabilities, our expert team is here to help you implement Databricks solutions tailored to your business needs.
Let us help you transform your data infrastructure into a powerful asset that drives innovation and growth. Contact us today to get started!
