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Our PL/SQL specialists build and maintain the full range of server-side database logic: packages, stored procedures, functions, triggers, and views, organised cleanly so logic is reusable and testable rather than scattered. They write set-based SQL first and only drop to procedural code where it genuinely belongs, use bulk processing with BULK COLLECT and FORALL to eliminate slow row-by-row loops, and design cursor handling, ref cursors, and pipelined table functions for streaming results. They build robust exception handling with named exceptions and proper logging instead of swallowing errors, use packages to expose clean APIs and hide implementation, and apply Oracle features like collections, records, dynamic SQL with bind variables, and autonomous transactions where appropriate. They also write unit tests with utPLSQL. The PM confirms your Oracle version and coding standards so the specialist matches your conventions from the first commit.

Yes, performance is one of the most common reasons teams hire us. The specialist starts with evidence: an AWR report to see the heaviest SQL, SQL Monitor to capture real execution plans for long-running statements, and DBMS_PROFILER or the hierarchical profiler (DBMS_HPROF) to find which PL/SQL lines and calls actually burn time. They eliminate row-by-row processing by rewriting loops as BULK COLLECT and FORALL, replace procedural logic with set-based SQL where possible, and stabilise volatile plans with SQL plan baselines rather than scattering hints. They check optimizer statistics and histograms, add or fix indexes, reduce context switches between the SQL and PL/SQL engines, and cache deterministic functions with the RESULT_CACHE. They measure before and after so the gain is provable. The PM confirms the workload and data volumes so the specialist targets the real bottleneck, not a guess.

Yes. Legacy PL/SQL cleanup is a frequent engagement. The specialist runs PL/SQL Hierarchical Profiler and reviews the code against PL/SQL warnings (PLW) to surface dead code, implicit conversions, and risky constructs, then refactors monolithic procedures into well-structured packages with clear APIs. They replace SQL injection-prone dynamic SQL with bind variables and DBMS_ASSERT, remove hard-coded values into constants and lookup tables, and add proper error logging where the original code silently swallowed exceptions. For modernisation they introduce utPLSQL unit tests so future changes are safe, add the PL/SQL function result cache and conditional compilation where it helps, and document the dependencies before touching anything. They use the data dictionary and DBMS_UTILITY to map the call graph so nothing breaks downstream. The PM scopes the codebase size and risk so remediation is staged sensibly with tests as a safety net.

A PM is assigned within 10 minutes of booking and scopes your requirement on the first call. For a clearly defined task a slow batch procedure, a broken trigger, a new package, a data-fix script a vetted PL/SQL specialist typically starts the same day. We offer three engagement models: project-based (a fixed deliverable like a new module or a tuning pass, with milestones), dedicated (a specialist embedded with your team for a sprint or longer, ideal for ongoing development), and on-call burst capacity (hours drawn down as production issues and change requests arise). You can scale up or down between engagements and cancel after any of them. The PM coordinates the specialist with your DBA and application teams so schema access, deployment windows, and code-review process are never a blocker.

Yes, and safely is the operative word. For data fixes and one-off corrections the specialist always writes the change as a reversible, logged script run inside a transaction with explicit validation counts before and after, never an ad-hoc UPDATE on production. For batch and ETL they design jobs scheduled with DBMS_SCHEDULER (chains, windows, and resource plans), process large volumes with BULK COLLECT and FORALL with SAVE EXCEPTIONS so a few bad rows do not kill the run, and add restartability so a failed job resumes rather than restarting from zero. They use external tables and Data Pump for loads, partition-aware processing for big tables, and proper commit sizing to balance undo and redo. Logging and instrumentation are built in so operations can see progress and failures. The PM confirms your batch windows and volumes so the design fits the real SLA.