Lakehouse architecture, Spark pipelines and governance from an official Databricks partner.
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§ Signs
As an official Databricks partner, we design and build Databricks lakehouses on Azure and AWS. Our Databricks data engineering team develops Apache Spark and PySpark pipelines, Delta Lake tables and medallion (bronze, silver and gold) layers, batch and streaming ingestion with Auto Loader, and Lakeflow Jobs and Declarative Pipelines. We put Unity Catalog governance, data quality checks and job monitoring in place, troubleshoot slow Spark workloads, and migrate SQL Server workloads to Databricks. The result is a lakehouse your analysts, data scientists and BI tools can rely on.
Databricks gives engineers a lot of freedom. Without structure, that freedom turns into cost and risk.
Business-critical jobs run from interactive notebooks with no tests, no review and no job definitions in code.
All-purpose clusters left running, no cluster policies, and job sizes chosen by guesswork.
Parquet and CSV folders with unclear owners, no schema enforcement and several copies of the same data.
Tables still in the Hive metastore, grants scattered across workspaces and no single view of who can read what.
Skew, small files and shuffle spills that keep coming back because the root cause was never addressed.
§ What we deliver
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AWS
dbt§ Proof
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§ Approach
We review your sources, current Spark or SQL workloads, cloud setup and governance needs.
You receive Databricks assessment and lakehouse roadmap
We design workspaces, the medallion layers, Unity Catalog structure and cluster policies.
You receive Lakehouse architecture and governance design
PySpark and SQL pipelines, Lakeflow jobs, Delta tables and dbt models, with tests and CI/CD.
You receive Tested pipelines deployed through CI/CD
Data quality rules and reconciliation against the source systems before consumers switch over.
You receive Data quality checks and reconciliation reports
We tune jobs and clusters, set up workflow monitoring and alerting, and support the platform.
You receive Performance report, monitoring and runbooks
We hand over the codebase, run walkthroughs of the jobs and the Unity Catalog model, and agree how the platform is supported from here.
You receive Runbooks, job alerts and recorded walkthroughs
§ Tech stack
dbtAzureAWSAzure DevOps§ Engagement models
A defined scope, timeline and deliverables, from design through go-live.
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Data engineers who join your team and work in your tools and processes.
Consultants and developers working remotely, with overlap in your working hours.
Architecture reviews, platform choices and design guidance for your own team.
Monitoring, incident handling and ongoing fixes for pipelines and databases in production.
A short, fixed-scope engagement to test an approach on your data or plan a migration.
§ FAQs
A layered lakehouse design: bronze holds raw ingested data, silver holds cleaned and conformed data, and gold holds business-ready tables for analytics and BI. We implement it on Delta Lake in Databricks.
Yes. We build batch and streaming data pipelines, including incremental ingestion with Auto Loader and Lakeflow Jobs and Declarative Pipelines.
Yes. SQL Server to Databricks migration is one of our specialized consulting services, covering data movement, code conversion to Spark SQL or PySpark, and reconciliation before cut-over.
Yes. Performance optimization and Spark troubleshooting covers partitioning, file layout, joins, caching, cluster sizing and job design, and is part of our Snowflake and Databricks platform optimization service.
Yes, both. We also integrate Databricks with Azure Data Factory, Power BI and dbt.
Yes. Equitysoft is an official Databricks partner.
We inventory tables, jobs and permissions, create catalogs and grants, upgrade tables in batches, repoint jobs and validate results before retiring the old paths. Each batch is reversible until it is signed off.
Serverless removes cluster management and starts quickly, which suits SQL, short jobs and spiky workloads. Classic compute gives more control over libraries and configuration. We review your workloads and usually recommend a mix.
Cluster policies, auto-termination, job compute for scheduled work, right-sized instances, Photon only where it pays back, and system tables that show DBU spend by job and team so costs have an owner.
Yes. We convert Hive and HDFS data to Delta tables, port and re-test jobs, and reconcile outputs between the old and new platforms before cut-over.
Yes. We set up MLflow tracking, models registered in Unity Catalog, feature tables and model serving, and work with our data science team when you need models built as well.
An assessment takes a few weeks, and a first medallion pipeline can usually reach production shortly after. A full migration depends on the number of jobs and their complexity, so we plan it in waves.
§ In detail
Workspaces, catalogs, schemas, medallion layers and environments, with naming rules your teams can follow.
Metastore, catalogs per domain, groups and grants, lineage, and a staged move of tables from the Hive metastore.
PySpark and SQL pipelines, Auto Loader ingestion and Lakeflow Declarative Pipelines with expectations, deployed with Databricks Asset Bundles through CI/CD.
Cluster policies, job compute instead of all-purpose clusters, serverless where it fits, and system-table dashboards for DBU spend.
Before and after timings for the heaviest jobs, with the file layout, clustering and configuration changes that produced them.
Job alerts, retry rules and an on-call runbook for the pipelines we built.
Jobs, pipelines and permissions live in Git, so any environment can be rebuilt and every change is reviewed.
We help your data scientists and analysts turn useful notebooks into tested, scheduled production jobs.
The Unity Catalog model is agreed with your data owners and security team, not imposed by us.
Project delivery, engineers embedded in your squad, or a periodic platform health check.
Sensor and machine data processed with Structured Streaming, feeding quality and predictive maintenance models.
Large historical datasets for risk and fraud features, with Unity Catalog lineage to explain where every number came from.
Clickstream and transaction data joined for demand forecasting and personalization, with experiments tracked in MLflow.
High-volume event feeds aggregated into features for analytics products and prediction models.
§ Contact
Starting a lakehouse, migrating from SQL Server, or fixing slow jobs? Tell us about it.
