We migrate data, code, pipelines and users to the Databricks Data Intelligence Platform, then prove every number matches before the old system is switched off. Our leadership has spent years inside IBM, Hadoop and cloud data estates, so we know both sides of the move.
Migrating to Databricks means moving data, SQL, ETL pipelines, schedules, security policies and users from platforms such as Hadoop, Redshift, BigQuery, Netezza or Teradata to a governed lakehouse. NeuralShore runs it in five phases: assess, plan, convert, reconcile, then cut over and optimize.
Cloudera, Hortonworks, MapR
Redshift, EMR, Glue, Athena
BigQuery, Dataproc, Dataflow
Netezza, Db2 Warehouse, DataStage, Cognos data sources
Teradata, Oracle, SQL Server
Informatica, SSIS, Talend
Engineering, BI, ML and agents run on the same governed data, so you stop copying it between tools.
Your data lives in Delta Lake and Apache Iceberg tables in your own cloud storage.
Unity Catalog covers access, lineage and audit for tables, models and agents.
Serverless compute scales up for peaks and down to zero when idle.
Genie, Agent Bricks and MLflow work on migrated data without another project.
Declarative pipelines, managed jobs and AI-assisted operations reduce manual run work.
Inventory every table, job, report and user. Map dependencies, find what is unused, and estimate effort and cost.
Output: Migration assessment and business caseDesign the target lakehouse, governance and security. Group workloads into waves by value and risk.
Output: Target architecture and wave planConvert SQL and ETL with LakeBridge and our accelerators, then review and test the output.
Output: Converted, tested code per waveRun old and new side by side and compare results table by table until they match.
Output: Signed-off reconciliation reportsMove users in waves, retire the old system, then tune cost and performance.
Output: Decommission plan and FinOps baselineWe use Databricks LakeBridge for profiling, code conversion and reconciliation, alongside our own accelerators and engineer review of every critical path.
Example journeys show how we run each type of migration. They are not named client results. Ask us for references from comparable projects.
An on-premise Hadoop cluster near end of support, with a backlog of regulatory and risk reports.
Read the journey: Hadoop to Databricks for a retail bank →An end-of-life appliance and hundreds of DataStage jobs feeding actuarial and claims reporting.
Read the journey: IBM Netezza and DataStage to Databricks for an insurer →Several AWS data services stitched together, with rising cost and slow fraud analytics.
Read the journey: AWS Redshift, EMR and Glue to Databricks for a fintech →Network and customer data split between BigQuery and Dataproc, with churn models built off-platform.
Read the journey: Google BigQuery and Dataproc to Databricks for a telco →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 →End-of-life appliances and heavy ETL estates are a strong case for moving. Our team knows both sides.
Read the article: IBM Netezza, Db2 and DataStage: modernizing to Databricks →Capacity-bound warehouses cost the most at peak. Elastic compute on the lakehouse changes the economics.
Read the article: Teradata, Oracle and SQL Server: replacing the legacy warehouse →A migration is approved on value, not technology. Here is how to build a case your CFO will sign.
Read the article: Building the business case for a Databricks migration →Yes. We migrate from Cloudera and Hortonworks Hadoop, AWS Redshift, EMR and Glue, Google BigQuery and Dataproc, IBM Netezza, Db2 and DataStage, and warehouses such as Teradata, Oracle and SQL Server.
We run the old and new platforms side by side and reconcile results table by table against matching rules agreed with business owners. Users move only after sign-off.
