Oracle Solutions of VA
Home / Data & Database

Database, data warehousing and data engineering

We keep databases fast, available and secure, and build the warehouses and pipelines that give your teams trustworthy numbers.

Database services

Database administration

Installation, patching, upgrades, backup and recovery, monitoring and security for production databases, on premises and in the cloud.

OracleSQL ServerPostgreSQLMySQLMongoDB

Performance tuning

Query, index and configuration tuning that resolves slow reports and batch windows without new hardware.

AWR / ASHExecution plansIndexingPartitioning

Migration and upgrades

Version upgrades and platform moves, including on-premise to AWS, Azure, Google Cloud or OCI, with tested cutover and rollback plans.

UpgradesCloud migrationAWS RDSAzure SQL

Database development

Schema design, stored procedures, PL/SQL and T-SQL packages, and data models built for the way your applications query.

Data modelingPL/SQLT-SQLNoSQL

Data warehousing and data engineering

Data warehousing

Dimensional models and enterprise warehouses that bring finance, operations and HR data into one place for reporting.

SnowflakeRedshiftBigQueryAzure SynapseOracle ADW

Data lake and lakehouse

Storage and processing for large and semi-structured data, from Hadoop and HDFS to modern lakehouse platforms.

DatabricksSparkDelta LakeHadoop / HDFSS3

Pipelines, ETL and ELT

Batch and streaming pipelines that extract from ERP, CRM, databases, APIs and files, with validation and alerting at every step.

InformaticaODISSISdbtAirflowKafka

BI and analytics

Dashboards and self-service reporting that give managers current numbers instead of spreadsheet exports.

Power BITableauOracle AnalyticsOBIEELooker

Data quality and governance

Profiling, cleansing, master data and access controls, so the numbers in every report can be traced and trusted.

Data profilingMDMLineageAccess controls

AI and ML data readiness

Clean, well-documented data sets and feature pipelines that machine learning and AI projects can be built on.

Feature pipelinesPythonVector searchMLOps

How a data project runs

  1. AssessSources, volumes, current reports and the questions the business needs answered.
  2. ModelTarget data model, platform choice and security design agreed with your team.
  3. BuildPipelines and transformations developed in iterations, with automated tests.
  4. ValidateNumbers reconciled against source systems before anyone relies on them.
  5. DeliverDashboards, documentation and training for analysts and administrators.
  6. OperateMonitoring, tuning and support as data volumes and needs grow.

Have a database or data project in mind?

Share a short description and we will suggest an approach and timeline.