Data Engineering Services
We design and build the pipelines, lakehouses, and governance layers that turn scattered data into analytics-ready systems on Databricks, AWS, Azure, or Google Cloud, whichever a client already runs.
Our Esteemed Clientele
Why Choose Data Engineering Services with Databricks by Sunflower Lab?
Sunflower Lab brings deep expertise in data engineering with Databricks, enabling businesses to use the full potential of their data. As a trusted Databricks Partner, with proven expertise across industries like manufacturing, finance & others, we specialize in designing and implementing modern data architectures that unify data, improve operational efficiency, and accelerate decision-making for measurable business outcomes.
Our Data Engineering Services
We provide end-to-end data engineering services powered by Databricks, including:
Data Collection and Ingestion
Data ingestion (pulling data out of databases, APIs, and live data streams and into one place) is the first step in any data engineering project. We land it in Azure Blob, AWS S3, or Google Cloud Storage, then run automated ingestion jobs that keep pulling new data without someone re-running a script.
Unified Data Storage & Lakehouse Solutions
A lakehouse - one platform that stores both raw and structured data, instead of running a separate data lake and data warehouse is the foundation we build on for most clients. It cuts the number of systems a team maintains and the cost difference between running two platforms and running one.
Data Quality and Cleaning
Bad data breaks AI models before they ship. We handle outlier detection, duplicate removal, and null-value handling as automated, reusable jobs, not a one-time cleanup so data quality holds as new data keeps arriving, not just on the day a pipeline is handed off.
Data Processing and Transformation
ETL pipelines (Extract, Transform, Load - the process that turns raw data into analytics-ready tables) run on Databricks’ Spark engine, which processes both scheduled batch jobs and live streaming data on the same infrastructure. That means one pipeline, not two, when a client needs both weekly reports and same-day alerts.
Automated Data Pipelines
Pipelines we build run on a schedule or trigger the moment new data arrives, without someone kicking off a script by hand. They’re built to add new data sources without a rebuild, a client going from 3 source systems to 12 shouldn’t mean starting over.
Data Governance and Compliance
Governance means role-based access control, an audit trail of who touched what data and when, and alignment with GDPR, HIPAA, or SOC 2, depending on the industry. We build this in from the first pipeline, not as a phase-two add-on after a compliance team flags a gap.
Streamlining Manufacturing with Databricks for Faster Data Processing
A global manufacturing leader faced challenges with fragmented data, inconsistent reporting, and slow processing across production, customer feedback, and supply chain operations, leading to costly delays. Partnering with Sunflower Lab, they leveraged our Databricks expertise to implement a custom data pipeline that unified their systems and accelerated processing. This solution reduced data processing time by 60%, enabling faster decision-making and streamlined operations.
Hire Databricks Engineer to make the most out of your Data!
Data Ready for Analytics & AI
Our Data Engineering with Databricks services ensure your data is optimized for advanced analytics and AI applications. Now, it’s time you integrate machine learning and real-time analytics capabilities, and make smarter, faster decisions for your business.
Machine Learning Integration
With our Data engineering services use the full potential of your data – build, train, and deploy predictive models that identify trends, forecast outcomes, and achieve insights. Whether it’s personalizing customer experiences, detecting anomalies, or optimizing processes, our scalable and collaborative environments enable faster innovation and deliver actionable insights that deliver impactful results.
Real-time Analytics & Insights
Achieve real-time data streaming with Databricks. From detecting fraud to optimizing supply chain operations, we provide actionable insights that enable you to make informed decisions with confidence and clarity, whenever it matters most.
Transforming Financial Services with Real-Time Data Insights
A financial services firm faced challenges with outdated data infrastructure, delaying insights and hampering risk management. By modernizing their data lake with Databricks, we enabled real-time data ingestion, processing, and advanced governance to ensure compliance. The enhanced infrastructure delivered real-time analytics, empowering faster decision-making and more efficient risk management, transforming their operations with data-driven precision.
See how others are making smarter decisions by choosing our Databricks Consultants!
Key Benefits of Partnering with Sunflower Lab for Databricks Engineering
Expertise and Certification
Our certified Databricks experts deliver efficient, scalable data solutions tailored to your needs, ensuring optimal performance.
Customized Solutions
We create Databricks solutions designed specifically for your business, aligning with your goals to maximize efficiency and integration.
Proven Track Record
With experience across industries like manufacturing, finance, and healthcare, we bring a proven track record of delivering reliable, scalable data solutions.
End-to-End Support
From strategy to deployment and ongoing maintenance, we provide comprehensive support at every stage of your Databricks journey.
Accelerated Time to Value
Through pre-built frameworks and automation, we shorten implementation timelines and deliver faster, actionable insights from your data.
Teams & Achievements
12+
Years of Experience
100+
Projects Completed
96%
Customer Retention
32+
Industries served
Explore Our More Capabilities
FAQ
Data engineering services cover the design and upkeep of the pipelines that move data from source systems (databases, apps, IoT devices) into a form ready for reporting and AI. That includes ingestion, transformation, storage in a data lake or lakehouse (one platform holding both raw and structured data), and the governance layer that keeps access controlled and audit trails intact. Sunflower Lab builds this on Databricks, AWS, Azure, or Google Cloud, matched to what a client already runs, rather than pushing one platform by default.
Sunflower Lab does — a US-based team with 12+ years of business-critical systems work and 100+ delivered projects across manufacturing, healthcare, and financial services. Pipelines are designed to scale from a few gigabytes to enterprise data volumes without a rebuild, using Databricks’ Spark-based processing (a distributed engine that processes large datasets in parallel) when the workload calls for it.
A single data engineer covers one skill set at a time — pipeline code, cloud infrastructure, or governance, rarely all three. A data engineering services firm brings a team that covers ingestion, transformation, lakehouse architecture, and compliance together, plus the delivery experience of having done it before. For a one-off internal tool, hiring a data engineer directly can work; for a production system feeding business decisions, most clients get there faster with a firm that’s shipped 100+ of these builds.
We inventory the source systems first — on-prem databases, aging ERP or MES exports, brittle warehouse scripts — then classify what moves first based on what’s breaking reporting today. Migrations run in phases with validation criteria defined before cutover, not after, so a client can roll back a phase without losing the whole project.
ROI gets defined before the project starts, not after: hours of manual reporting eliminated, decision-making time cut, or new revenue opportunities the data made visible. We track against that number through delivery, not just an on-time/on-budget measure, because a pipeline that ships on schedule but doesn’t move the number it was built for hasn’t paid for itself yet.
A data lake stores raw data cheaply in its original format; a data warehouse stores cleaned, structured data optimized for fast reporting; a lakehouse combines both — raw and structured data on one platform, at data-lake cost with data-warehouse query performance. Most Sunflower Lab engagements land on a lakehouse architecture because it avoids maintaining two separate systems for the same data.
Yes. Real-time (streaming) pipelines process data as it arrives instead of waiting for a nightly batch job, using tools like Apache Kafka (a message queue that moves data between systems) and Spark Structured Streaming (Databricks’ real-time processing engine). This matters for use cases like fraud detection or supply-chain alerts, where a same-day report is too slow.
Governance is built into the first pipeline, not added after a compliance review flags a gap: role-based access control, an audit trail of who touched what data and when, and alignment with GDPR, HIPAA, or SOC 2 depending on the industry. For regulated clients in healthcare and financial services specifically, access controls and encryption are configured before the first real dataset lands in the environment.
From Ideation To Support, We Partner With You All The Way
Contact our team of experts today!
