Datrick
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Senior data operations delivery
Datrick helps CTOs and IT service firms restore technical ownership, operate database environments, support migrations, and make BI and analytics delivery reliable.
What success means
Systems, access, dependencies, recurring work, reporting commitments, known issues, decision routes, and escalation paths are visible enough for an authorized team to operate and change responsibly.
Monitoring signals, severity, coverage, backup and restore expectations, on-call boundaries, status communication, evidence capture, and follow-up actions are explicitly assigned.
Readiness, data validation, output comparison, performance, reporting continuity, approval, cutover, rollback, and handover are treated as operating responsibilities rather than last-minute checks.
KPI definitions, source ownership, refresh schedules, access rules, transformation logic, quality checks, performance behavior, and stakeholder outputs are documented and reviewable.
Entry routes
Delivery evidence
A warehouse optimization engagement reviewed query patterns, model structure, scan behavior, refresh ownership, and reporting-layer maintainability. Measured query time fell by 68%.
The same engagement reduced unnecessary data scanning by 85%, improving both reporting responsiveness and the operating team's ability to explain workload behavior.
An IT operations partner began with urgent database and migration needs. Prompt, documented execution led to additional BI, reporting, analytics, and operations work while the partner retained the client relationship.
The partner's identity, end-client identity, environments, workloads, rates, contract terms, and confidential operational metrics are not published. The operating pattern is shared without inventing public proof.
Core operating controls
Named accounts, least privilege, secure credential channels, environment boundaries, approval, expiry, revocation, and emergency procedures are agreed with the authorized owner.
Support windows, severity, response expectations, on-call duties, communication routes, escalation, and exclusions are written into the operating model.
Request, approval, implementation, validation, rollback, evidence, and stakeholder communication are assigned before critical changes are executed.
Systems, jobs, dashboards, runbooks, vendors, incidents, risks, and business outputs have accountable owners and an escalation path.
Status notes, assumptions, findings, changes, tests, risks, known issues, runbooks, and acceptance evidence reduce reliance on memory.
Repeated incidents, slow workflows, manual checks, unclear reports, and missing documentation are prioritized instead of rediscovered indefinitely.
Phase-gated onboarding
Describe the business risk, service area, environment, workload, urgency, stakeholders, current ownership, and what has already been tried.
Map systems, environments, authorized access, dependencies, monitoring, repositories, recurring work, reports, vendors, and active changes.
Separate critical continuity gaps from high, moderate, and planned improvement work using operating consequences and available evidence.
Confirm coverage, priorities, communication, access, approvals, deliverables, acceptance, escalation, documentation, and commercial boundaries.
Execute the prioritized scope through a named lead, surface blockers early, preserve evidence, and maintain reviewable status.
Continue into an operations program, extend a project, or complete a documented transfer based on demand and observed results.
Engagement shapes
A bounded production issue, failed handover, migration blocker, reporting failure, or immediate operational risk.
Typical outputControlled intake, evidence, risk, stabilization actions, ownership map, and recommended next scope.Migration QA, BI reliability, performance optimization, pipeline delivery, documentation, or another defined outcome.
Typical outputPlan, deliverables, implementation evidence, validation, risks, runbook updates, and handover.Recurring database, migration, reporting, analytics, and improvement responsibilities with agreed coverage and cadence.
Typical outputNamed ownership, prioritized backlog, status, incident context, documentation, and senior review.Data operations FAQ
Scope can include database operations and NOC coverage, backup and restore coordination, incident support, database handover recovery, migration development and QA, BI reporting reliability, analytics, pipelines, integrations, performance tuning, runbooks, and operational improvement. Final coverage is documented for each engagement.
Datrick can begin with an evidence-led handover recovery: inventory systems, map authorized access, identify dependencies, review monitoring and recurring work, classify risk, create runbooks, and establish named ownership. Missing information is recorded as risk rather than guessed.
Yes. Many engagements begin with one bounded problem such as an incident, failed handover, migration blocker, slow reporting workload, unreliable dashboard, or missing pipeline owner. Datrick then recommends whether to close the scope, extend the project, or establish ongoing coverage.
Coverage windows, on-call responsibilities, severity definitions, response expectations, escalation paths, and staffing depend on the engagement. Datrick does not represent every data operations engagement as automatic 24/7 support; the required coverage model must be scoped and confirmed in writing.
Migration support can cover a defined workstream such as development, QA, reconciliation, performance review, reporting continuity, cutover readiness, rollback evidence, or handover. Full migration ownership adds broader architecture, program governance, stakeholder, dependency, and acceptance responsibilities and must be scoped separately.
Onboarding normally requires named stakeholders, a system and environment inventory, authorized access paths, monitoring context, known incidents, backup and restore expectations, repositories, recurring work, reporting dependencies, active change plans, communication routes, and approval authority. Sensitive credentials should never be sent through the website form.
Operations library
Prepare systems, access, backups, monitoring, recurring work, escalation, risks, and reporting dependencies.
Prepare QA, validation, reporting continuity, cutover, rollback, stakeholder updates, and operating handover.
Prepare KPI definitions, dashboard ownership, refresh cadence, access, data quality, and stakeholder review.
Connect waits, locks, queries, plans, platform evidence, and changes into a safe, supervised DBA investigation workflow.
Use run evidence, lineage, quality, output state, and controlled recovery policies to restore failed data workflows safely.
Build layered source-to-target validation, exception evidence, CDC checks, and an accountable cutover gate.
Detect stale or inconsistent reports with source cutoffs, refresh evidence, KPI controls, impact, and accountable recovery.
Manage refresh failures, schedules, gateway HA, credentials, capacity overlap, incremental partitions, freshness SLAs, and validated recovery.
Connect CU smoothing, throttling, operations, workspaces, items, users, performance, schedules, and Azure cost into an approval-ready capacity decision.
Find the measured bottleneck across visuals, DAX, model structure, storage mode, Power Query, sources, gateways, and capacity, then validate correctness and user latency.
Build a verified inventory, restore ownership, review effective access, classify lifecycle, and operate evidence-backed archive and remediation decisions.
Replace manual publishing with controlled source, environment binding, dependency sequencing, testing, approval, rollback, and post-deployment validation.
Map source and target tenants, artifacts, identities, gateways, dependencies, data, security, links, cutover, validation, and post-migration operations.
Audit workspace roles, Build permission, app audiences, direct grants, external sharing, service principals, RLS, and source access before controlled remediation.
Add incident ownership, monitoring, complex escalation, controlled changes, backlog delivery, SLA reporting, and service improvement under your brand.
Operate client agents under the partner's brand with quality regression, telemetry, retrieval, tool, identity, incident, release, cost, and reporting controls.
Restore ownership, reconstruct undocumented dependencies, validate business outputs, stabilize personal credentials, and establish a supportable operating model.
Inventory and rationalize the Tableau portfolio, map semantic logic, prove a representative pilot, validate parity, and cut over with controlled adoption.
Assess catalog usage and compatibility, migrate supported RDLs, redesign exceptions, rebuild distribution, validate rendering, and retire report-server dependencies.
Test service principal profiles, customer mapping, RLS identity, embed tokens, lifecycle automation, capacity SLOs, and cross-customer isolation.
Prepare governed semantic models, configure AI context, evaluate answers against deterministic controls, and roll out Copilot by use case and risk.
Choose the first production workload, establish architecture and operating controls, validate capacity economics, and sequence a 90-day implementation plan.
Prove item-level protection, reconstruction, recovery sequencing, security, reconciled data, RPO, RTO, and business acceptance with a controlled drill.
Give production workloads accountable monitoring, incident response, capacity and cost control, reliable change, backlog delivery, and continual improvement.
Classify every Synapse workload, test migration-tool coverage, prove a production-shaped pilot, reconcile parity, and execute reversible migration waves.
Ground conversational agents in governed semantic, SQL, and KQL sources; enforce user permissions and prove answer quality with deterministic evaluation.
Turn one business domain into governed entities, relationships, source bindings, and measurable ontology grounding for enterprise agents.
Convert one operational signal into a tested playbook, safe recommendation or action, Teams approval, audit evidence, and measured response improvement.
Prove effective access across workspace and OneLake roles, RLS, CLS, SQL identity modes, Spark, Direct Lake, shortcuts, and Data Agents.
Validate sensitive grounding data, interaction controls, audit evidence, risk monitoring, retention, eDiscovery, licensing, and incident response.
Connect Data Agent, Operations Agent, Copilot, generated-query, storage, and action consumption to quality, unit economics, SLOs, and capacity risk.
Control Git definitions, draft and published stages, source rebinding, dev/test/prod promotion, evaluation gates, drift, and rollback.
Contain unreliable answers, isolate routing, query, identity, policy, publishing, integration, and capacity failures, then restore a tested service path.
Expose governed OneLake knowledge to approved AI clients with tested identity, permissions, data boundaries, tool routing, answer quality, and operating controls.
Give custom applications and background services governed Fabric answers through a least-privilege service principal, resilient MCP adapter, and production quality controls.
Partition specialist data domains, route intent, preserve identity, synthesize attributable answers, isolate actions, and recover individual agent routes.
Prove Advanced NL2SQL gains, catch query and answer regressions, and control runtime republishing, monitoring, and rollback.
Tighten schema scope, join and date rules, categorical values, few-shot examples, generated SQL, deterministic results, security, and regression gates.
Validate question-query pairs, Clarity, Relatedness, Mapping, schema execution, run-step retrieval, collision risk, generated SQL or KQL, security, and lifecycle.
Define the English-only support boundary and validate any translation layer across terminology, intent, generated queries, values, locale rules, security, disclosure, and rollback.
Place routing, query, semantic, policy, and response rules in the configuration layer that can use them, then test draft and published behavior.
Validate creator permissions, Teams approvers, parameter controls, downstream execution, expiry, identity lifecycle, evidence, containment, and recovery.
Reconcile generated queries, source and time semantics, state and transition conditions, replay, false positives, missed events, duplicate messages, and monitoring.
Separate query and configuration access, govern OAuth identities and write-capable tools, test source and action changes, and establish audited recovery controls.
Validate ontology entities, properties, relationships, live bindings, graph freshness, generated rules, source permissions, action parameters, and monitoring evidence.
Version public definitions, rebind environment resources, handle delegated identity and long-running updates, deploy inactive, run behavioral gates, and prove rollback.
Contain unsafe automation and reconcile source, query, rule, operation, Teams, identity, approval, action, capacity, change, recovery, and reactivation evidence.
Use workspace Eventhouse logs, deterministic alerts, Operations Agent context, Teams escalation, replay, capacity checks, and NOC ownership for critical pipeline runs.
Prove event-time windows, entity state, selected KQL schema, functions, examples, generated queries, user permissions, capacity, and answer fidelity.
Prove node and edge semantics, path direction and modes, source instructions, example GQL, generated traversals, refresh, permissions, capacity, and answer evidence.
Prove document authority, index and chunk design, retrieval relevance, citations, user access, source routing, answer groundedness, freshness, and failure behavior.
Turn schema and query-history suggestions into reviewed instructions, validated few-shots, measured query and answer regressions, controlled releases, and rollback evidence.
Prove source authority, configuration boundaries, route selection, tool output, combined evidence, effective access, runtime changes, and route-level rollback.
Test semantic model metadata, Prep for AI, verified answers, generated DAX, business correctness, RLS and CLS, performance, release, and rollback.
Prove trigger precision and recall, filter behavior, visual and DAX grounding, conflicts, model dependencies, consumer security, cross-experience behavior, and rollback.
Version ground truth, automate SDK runs, calibrate critics, inspect step evidence, compare sandbox and production, and block unsafe regressions.
Prove source and input completeness, generated Python, numeric and visual correctness, effective access, sandbox governance, latency, and release safety.
Test source queries, 200-row truncation, stable ordering, chart selection, labels, permissions, accessibility, client compatibility, release, and rollback.
Prove 25-by-25 output boundaries, multi-turn reference resolution, new-chat behavior, history persistence, effective permissions, query fidelity, and regression safety.
Align agent and source capacities, tenant settings, AI processing and storage boundaries, client data flows, capacity, migration controls, validation, and rollback.
Preserve user permissions across Foundry orchestration, Fabric source routing, generated queries, answer synthesis, tracing, evaluation, and production support.
Run tracing, evaluation, retrieval, tools, MCP, identity, incident response, controlled releases, telemetry security, and cost as one service.
Operate persistent sessions, event streams, MCP and tools, cloud or self-hosted sandboxes, credentials, incidents, releases, cost, and beta risk.
Operate application runtimes, traces, sessions, tools and MCP, handoffs, guardrails, approvals, incidents, releases, and cost.
Operate runtime metrics, quality, sessions, memory, IAM, private networking, tools, incidents, releases, quotas, and cost.
Operate runtime sessions, traces, evaluations, identities, gateways, tools, memory, policy, incidents, releases, quotas, and cost.
Operate sessions, topics, actions, flows, Apex, permissions, Data 360, incidents, controlled releases, quality, and consumption.
Operate agentic workflows, execution plans, agents, tools, ACLs, identities, analytics, incidents, releases, and Assist consumption.
Operate sessions, evaluation, agent teams, tools, Fusion roles, OAuth integrations, incidents, quarterly releases, and outcomes.
Operate traces, tools, approvals, BTP environments, destinations, transport, deployment drift, incidents, releases, and consumption.
Operate messages, traces, tools, workflows, knowledge, connections, credentials, channels, incidents, versions, and outcomes.
Operate MLflow traces, evaluation, Model Serving, governed retrieval and tools, Unity Catalog identity, incidents, releases, and cost.
Operate request traces, evaluations, Analyst and Search tools, semantic data, custom actions, default-role access, incidents, releases, and cost.
Operate API and queue workers, durable threads and checkpoints, Postgres, Redis, tracing, evaluation, tool actions, incidents, and releases.
Operate crews and flows, state, triggers, memory, tools, API and worker workloads, external PostgreSQL and storage, incidents, releases, and cost.
Operate document sources, parsing, index synchronization, embeddings, vector stores, hybrid retrieval, tenant filters, incidents, releases, and cost.
Operate index and namespace design, ingestion freshness, tenant isolation, retrieval performance, Dedicated Read Nodes, monitoring, backups, incidents, and cost.
Operate collections, asynchronous indexing, shard and replica consistency, tenant lifecycle, retrieval performance, monitoring, backups, and cost.
Operate collections, shards, replicas, consistency and ordering, tenant routing, optimizer pressure, monitoring, backups, incidents, and cost.
Operate ingestion freshness, segments, indexes, collection loading, QueryNode replicas, resource groups, tenant isolation, monitoring, backups, and cost.
Operate vector schemas, HNSW and IVFFlat recall, filtered queries, planner behavior, vacuum and reindex, replication, restore, incidents, and cost.
Operate source ingestion, indexers, skillsets, vector and hybrid retrieval, semantic ranking, security, capacity, releases, resilience, and cost.
Operate ingest pipelines, mappings, aliases, shard health, vectors and inference, hybrid relevance, security, snapshots, upgrades, and cost.
Operate provisioned domains and Serverless collections, ingestion, k-NN relevance, shards or OCUs, IAM and VPC, monitoring, snapshots, and cost.
Operate collection-to-index freshness, mappings, ANN and ENN recall, Search Nodes, tenant filters, private networking, recovery, and cost.
Operate source keys, Search indexes and aliases, exact and approximate recall, filters, shards, memory, persistence, restore, and cost.
Operate data updates, ScaNN recall, filters, index and endpoint releases, shards, replicas, private connectivity, quotas, and cost.
Operate source items, partition and tenant design, DiskANN recall, indexing policies, RU/s, 429s, consistency, failover, restore, and cost.
Operate source sync, document ingestion, chunking, embeddings, vector-store state, retrieval, citations, security, incidents, and cost.
Operate corpora, file imports, chunking, embeddings, managed or external vector stores, retrieval, reranking, grounded answers, security, quotas, and cost.
Operate repository connectors, document sync and deletion, enrichment, ACLs, Query and Retrieve relevance, capacity, monitoring, incidents, and cost.
Operate source crawls, external items, schema, ACL and identity mapping, indexed content, Copilot discovery, throttling, incidents, rollout, and governance.
Operate connected knowledge, permission and deletion sync, Search and Chat outcomes, agent scope, tool actions, MCP access, incidents, and governance.
Operate datasource crawl and index coverage, permission synchronization, search relevance, Assistant answers, agent actions, background runs, and governance.
Operate sources, item and permission freshness, security identities, query pipelines, ML relevance, generative answers, performance, incidents, and usage.
Operate source-to-index tasks, atomic releases, replicas, settings, NeuralSearch relevance, analytics events, key security, rate limits, and cost.
Operate data streams, DLO and DMO mappings, chunks, embeddings, vector and hybrid indexes, retrievers, citations, permissions, quality, and credits.
Operate source indexing, search profiles and applications, relevance, Genius Results, external permissions, grounded answers, analytics, incidents, and releases.
Operate repository scope, pipeline ingestion, schedules, document limits, chunking, metadata, access, answer quality, AI Units, incidents, and releases.
Operate SharePoint and connector knowledge, manifest scope, permission trimming, licenses, context limits, citations, incidents, releases, and governance.
Operate files and Hubs, permissions, agent instructions, API completeness, extraction confidence, metadata writes, integrations, AI Units, and releases.
Operate built-in and custom sources, user account connections, OAuth, workspace grants, permission-aware retrieval, citations, incidents, and releases.
Operate connector scope, OAuth and consent, content and permission sync, searchable types, extraction limits, AI answers, audit, incidents, and releases.
Prove connected-agent authentication, source permissions, generative orchestration, Teams or website channel behavior, answer quality, and policy.
Operate session quality, knowledge, tools, triggers, identities, data policies, releases, incidents, Copilot Credits, and client-facing service evidence.
Move governed Fabric answers into Agent Store and Teams with proven discovery, user access, response fidelity, visualization, support, and adoption.
Turn failed quality checks into evidence-backed diagnosis, impact analysis, reversible containment, and independently validated recovery.
Find affected pipelines, reports, APIs, models, exports, and customers before a schema or contract change is released.
Combine explainable entity matching, false-merge controls, survivorship, human approval, downstream reconciliation, and rollback.
Give data owners effective-access, usage, sensitivity, purpose, and ownership evidence before retain, reduce, or revoke decisions.
Turn approved policies and requests into scoped actions, hold handling, platform-aware execution, downstream evidence, and verified closure.
Turn audit streams into context-rich cases with identity attribution, sensitive-object risk, change evidence, and controlled response.
Prepare patch windows with deterministic prechecks, dependency evidence, baselines, rollback controls, and post-change service validation.
Validate role transition, DNS, client recovery, data integrity, application transactions, RPO, RTO, fencing, and failback end to end.
Correlate pool acquisition, proxy waiting, database sessions, long transactions, locks, retries, capacity, and customer impact.
Turn native deadlock graphs and blocking chains into grouped patterns, safe response, validated fixes, and recurrence evidence.
Detect material plan changes with representative runtime evidence, controlled containment, automatic unforce conditions, and permanent remediation.
Prioritize targeted statistics work from distribution, sample, estimate, plan, workload, maintenance, and post-change evidence.
Replace fixed rebuild schedules with engine-aware condition, workload impact, execution risk, cost, and outcome evidence.
Forecast capacity limits across data, logs, temp, backups, and replicas, then route evidence-backed scale or remediation before risk becomes an incident.
Locate transport and apply bottlenecks, quantify stale-read and recovery exposure, and validate controlled remediation.
Find the engine-native reuse blocker, protect availability, preserve recovery, and validate durable remediation without unsafe purge or shrink actions.
Resolve exact DDL against engine behavior, production concurrency, dependencies, resources, compatibility, rollback, and measurable deployment gates.
Find which settings differ across desired, persisted, runtime, pending, and fleet states, then validate controlled correction against service outcomes.
Inventory server and CA certificates, client trust, verification modes, connection paths, restart risk, rehearsal, rollback, and post-rotation evidence.
Coordinate account, secret version, consumer refresh, connection-pool renewal, job testing, rollback, old-access revocation, and application proof.
Move beyond vendor prechecks with extension, SQL, driver, topology, workload, performance, downtime, rollback, and production transaction evidence.
Profile representative demand, compare target architectures and total-cost scenarios, then validate the leading size with production-relevant workload.
Find which workloads can safely share capacity using correlated peaks, platform compatibility, recovery, security, licensing, and concurrent tests.
Build reviewable deployment, feature, core, edition, cloud benefit, workload, cost, and target evidence while authorized owners interpret rights.
Join provider billing, resource identity, database usage, shared cost pools, versioned rules, reconciliation, showback, and chargeback evidence.
Map every retained copy to recovery purpose, owner, chain, policy, restore evidence, legal controls, cost, approval, and verified outcome.
Reconcile regional topology, replication and application flows, egress, lag, RPO, RTO, routing, failover capacity, and tested cost outcomes.
Reconcile eligible usage, utilization, coverage, expiry, stable workload, architecture roadmaps, commercial scenarios, and authorized renewal decisions.
Classify development and test demand, implement guarded schedules, manage exceptions, verify restore, and retire expired environments without hidden dependencies.
Compare target limits, replay representative workload, validate maintenance and failover, reconcile commitments, and control production change and rollback.
Balance minimum and maximum capacity, scale-up speed, pause and resume, background wake-ups, readers, failover, SLOs, and effective billing.
Reconcile actuals, model technical cost drivers, incorporate plans and commitments, quantify uncertainty, and route explainable variance to accountable owners.
Separate capacity from performance, validate latency and queueing, test backup and recovery, price the complete topology, and control production changes.
Trace metrics, logs, plans, alerts, retention, exports, privacy, incidents, stores, and billing; test lower-cost settings without losing operational evidence.
Connect every standby, readable replica, zone, tier, and proxy to funded failure modes, degraded capacity, application failover, RPO, RTO, and realized cost.
Trace every paid replica to real endpoints, clients, query classes, consistency limits, peak demand, workload isolation, promotion or DR value, and realized billing.
Connect every audit event and retained copy to a control, investigation, sensitive-data boundary, integrity test, retrieval objective, performance result, and realized invoice.
Connect engine deadlines, paid-support year tiers, vCPU and topology exposure, compatibility blockers, delivery capacity, exceptions, upgrade milestones, and realized invoices.
Measure client-to-backend reuse, pinning, borrow waits, session correctness, failover recovery, database headroom, endpoint cost, edition premium, and realized billing.
Written scoping
A senior lead will review the environment, urgency, ownership, workload, access constraints, and expected outcome, then respond with qualifying questions or a recommended starting shape.