Capacity-bound warehouses cost the most at peak. Elastic compute on the lakehouse changes the economics.
Replacing Teradata, Oracle or SQL Server with Databricks swaps peak-sized capacity for elastic compute. Data copies easily; the real work is converting procedural logic such as BTEQ, PL/SQL and T-SQL, then reconciling results. Agree matching rules with business owners before conversion starts, so sign-off is fast.
Teradata, Oracle and SQL Server warehouses still power many enterprises. Their logic is often buried in stored procedures, BTEQ scripts and PL/SQL.
Traditional warehouses are sized for peak demand, so you pay for capacity even when it is idle. Elastic and serverless compute on Databricks scales with the work.
Copying data is straightforward. Converting decades of procedural logic is not. We use LakeBridge and our accelerators for conversion, then review and test every critical path.
Users trust the new platform only when numbers match. We agree matching rules with business owners up front and report progress table by table.
Once migrated, the same data supports streaming, ML and agents. That is where the long-term value of the move comes from.
NeuralShore Engineering, Databricks delivery team
Databricks architects, data engineers and AI engineers at NeuralShore, DLytica’s Databricks delivery company. Leadership comes from IBM data and AI architecture.
Often, because compute scales with use instead of being sized for peak. We model cost on your real workloads.
Converting procedural logic such as BTEQ, PL/SQL and T-SQL, and proving results match.
Hadoop clusters are expensive to keep alive and hard to staff. Here is how to move without breaking the reports that depend on them.
Read the article: Leaving Hadoop: a step-by-step path to Databricks →You do not have to leave AWS to simplify your data platform. Consolidate several services into one governed lakehouse.
Read the article: From Redshift, EMR and Glue to Databricks on AWS →Run Databricks on Google Cloud next to your data, and bring analytics, engineering and ML onto one platform.
Read the article: BigQuery and Dataproc to Databricks on Google Cloud →
