Data & AI

The first things that break are the custom orchestration dependencies and the ADF pipelines with complex parameterization that do not map cleanly to Fabric Data Factory’s current feature parity. Synapse dedicated SQL pools also lack a direct Fabric equivalent, but the migration path runs through Fabric Warehouse or Lakehouse, depending on your query patterns and whether you need full DW semantics or can tolerate a lakehouse access model. We start every Fabric assessment with a dependency inventory that categorizes each existing component by migration complexity: lift-and-shift, refactor required, or rebuild, and sequences the migration to protect the pipelines and reports your business runs on daily. We also validate that your Power BI semantic models perform equivalently on Fabric before any Synapse decommission is on the table.

Unity Catalog migration is primarily a permissions and namespace problem. The legacy Hive metastore uses a two-level namespace: a database and a table. Unity Catalog uses a three-level namespace: catalog, schema, and table. This means every existing reference in notebooks, jobs, and pipelines needs to be remapped. We run an automated scan of your existing workspace to identify every hard-coded reference, categorize them by job criticality, and build a migration sequence that starts with non-production workloads where failures are recoverable. For production jobs, we implement a parallel run period where both metastore references are valid simultaneously, validate output parity, and only cut over after confirmation. Access policy migration is designed to work alongside the namespace migration, so you do not have to rebuild permissions from scratch after the structural change is complete.

Organic pipeline growth almost always produces the same pathology: inconsistent error handling, no retry standards, alerting that goes to inboxes nobody monitors, and failure states that cascade silently until a business user notices a report is wrong. We start with a pipeline audit that categorizes each pipeline by business criticality, current failure rate, and downstream impact, immediately giving you a prioritized remediation backlog. Standardization happens through a pipeline framework that defines retry logic, dead-letter-queue handling, alerting standards, and SLA monitoring as reusable patterns that teams adopt rather than reinvent. We implement the framework on your highest-criticality pipelines first to demonstrate the operational improvement, then roll it out as the standard for all new development and for existing pipeline refactors.

The two things most teams underestimate are storage throughput and network fabric. DGX systems consume data at rates that most existing storage platforms cannot sustain during training runs. You need a parallel file system or high-throughput object storage with enough IOPS and bandwidth to feed the GPUs without creating a storage bottleneck that negates the compute investment. On the network side, the interconnect between nodes for distributed training needs to be InfiniBand or a high-bandwidth Ethernet standard such as 10GbE, which creates training bottlenecks that become apparent only when you run your first multi-node job. The data pipeline architecture must also account for data staging. Raw training data needs to be preprocessed and staged close to the compute before training begins, not streamed from a remote source during the run. We design the full stack around the DGX deployment before it arrives, so the infrastructure is ready to actually use the compute on day one.

The key is separating data quality rules from pipeline logic, which most ad hoc implementations fail to do. When quality checks are embedded directly in pipeline code, they multiply with every new pipeline and become impossible to govern consistently. We implement a data quality framework using a rules engine, such as Great Expectations, Soda, or the native quality capabilities in your platform, where rules are defined centrally, versioned, and applied across pipelines through a shared library rather than duplicated in each one. The rule taxonomy is tiered by severity: critical checks that fail the pipeline, warning checks that log and alert without blocking, and informational checks that populate a data quality dashboard. Coverage is prioritized by business impact rather than pipeline count. Complete coverage of your ten most critical data products delivers more business value than partial coverage of everything. The maintenance burden scales with the number of rules, not the number of pipelines, which is the inversion that makes the framework sustainable.