Databricks Lakehouse architecture, Delta Lake migration, Unity Catalog governance, and MLflow operationalization for enterprise data teams at scale.
Whether they will tell you not to use Databricks. It suits heavy transformation, streaming and ML on the same data, and it is more platform than a team running SQL-centric reporting for a handful of BI users needs. A Databricks partner whose recommendation never varies by workload is describing its own certification rather than your problem. After that: who holds Unity Catalog and the governance model, whether they size compute to the workload or leave you discovering it on the bill, and whether the people who scope the work deliver it.
Lakehouse and medallion architecture, the transformation layer, Unity Catalog and the access model, compute sizing, and the handover that lets your team run it. The part that gets skipped is cost. Databricks makes it easy to run an expensive job on a cluster nobody sized, and a bill that doubles quietly is the most common reason these engagements get revisited. Good Databricks consulting services leave the cluster policies, the job schedules and the cost attribution documented, so your team can hold the number without us.
The Databricks Lakehouse combines the flexibility of a data lake with the reliability and performance of a data warehouse into a single platform. It supports SQL analytics, machine learning, and streaming workloads on a unified data layer, eliminating the need to maintain separate systems for each use case.
We design and implement Databricks Lakehouse architectures, migrate legacy data warehouses to Delta Lake, configure Unity Catalog for governance, and build ML pipelines using MLflow. We also integrate Databricks with your existing BI tools including Tableau and Power BI.
Unity Catalog is Databricks' unified governance solution for data and AI assets. It provides centralized access control, audit logging, lineage tracking, and data discovery across all your Databricks workspaces. For organizations with compliance requirements, Unity Catalog is essential.
Yes. We've migrated from Teradata, Netezza, SQL Server, and Snowflake to Databricks Delta Lake. We assess your current environment, design the target architecture, and execute the migration with minimal disruption to your existing analytics workflows.
Lakehouse consulting is the design and build of a Databricks Lakehouse: Delta Lake storage, Unity Catalog governance, and the SQL and ML layers on top. We architect it, migrate your existing data onto it, and hand your team a governed platform they can run, instead of a proof of concept that stalls.
It depends on the scope. A Lakehouse build, a warehouse migration, and ongoing cost optimization are different engagements. We scope against fixed milestones before any work starts, so you see the number before you commit. Cluster and warehouse cost optimization is part of every engagement, and most clients recover meaningful compute spend in the first few months.
Whether they will tell you not to use Databricks. It suits heavy transformation, streaming and ML on the same data, and it is more platform than a team running SQL-centric reporting for a handful of BI users needs. A Databricks partner whose recommendation never varies by workload is describing its own certification rather than your problem. After that: who holds Unity Catalog and the governance model, whether they size compute to the workload or leave you discovering it on the bill, and whether the people who scope the work deliver it.
Lakehouse and medallion architecture, the transformation layer, Unity Catalog and the access model, compute sizing, and the handover that lets your team run it. The part that gets skipped is cost. Databricks makes it easy to run an expensive job on a cluster nobody sized, and a bill that doubles quietly is the most common reason these engagements get revisited. Good Databricks consulting services leave the cluster policies, the job schedules and the cost attribution documented, so your team can hold the number without us.
The Databricks Lakehouse combines the flexibility of a data lake with the reliability and performance of a data warehouse into a single platform. It supports SQL analytics, machine learning, and streaming workloads on a unified data layer, eliminating the need to maintain separate systems for each use case.
We design and implement Databricks Lakehouse architectures, migrate legacy data warehouses to Delta Lake, configure Unity Catalog for governance, and build ML pipelines using MLflow. We also integrate Databricks with your existing BI tools including Tableau and Power BI.
Unity Catalog is Databricks' unified governance solution for data and AI assets. It provides centralized access control, audit logging, lineage tracking, and data discovery across all your Databricks workspaces. For organizations with compliance requirements, Unity Catalog is essential.
Yes. We've migrated from Teradata, Netezza, SQL Server, and Snowflake to Databricks Delta Lake. We assess your current environment, design the target architecture, and execute the migration with minimal disruption to your existing analytics workflows.
Lakehouse consulting is the design and build of a Databricks Lakehouse: Delta Lake storage, Unity Catalog governance, and the SQL and ML layers on top. We architect it, migrate your existing data onto it, and hand your team a governed platform they can run, instead of a proof of concept that stalls.
It depends on the scope. A Lakehouse build, a warehouse migration, and ongoing cost optimization are different engagements. We scope against fixed milestones before any work starts, so you see the number before you commit. Cluster and warehouse cost optimization is part of every engagement, and most clients recover meaningful compute spend in the first few months.
Databricks consulting: Lakehouse architecture, Delta Lake migration, Unity Catalog governance, and ML pipeline design. Senior-led engagements.
Build the semantic layer on top of your Databricks Lakehouse.
We design and implement Databricks Lakehouse architectures, migrate legacy data warehouses to Delta Lake, and build ML pipelines that run reliably in production. No prototypes. No unnecessary platform replacements.
Databricks consulting is the design, build, and migration of the Databricks Lakehouse: Delta Lake storage, Unity Catalog governance, and the SQL, BI, and ML layers on top. Thinklytics architects the platform, migrates your legacy warehouse onto it, and leaves your team a production system they can run, not a prototype.
We work across the full Databricks stack, from Lakehouse architecture design and Delta Lake migration to Unity Catalog governance and MLflow-based ML pipelines. Every engagement ends with a production deployment and a team that can operate it independently.
Start with a 30-day Analytics Truth Audit. We assess your current environment and give you a 90-day Databricks roadmap.