Lakehouse architecture
Data lake flexibility with data warehouse performance: SQL analytics, Python/Spark and ML on one platform, no duplication across warehouses and lakes, infrastructure costs down 40-60% and seamless analytics-to-ML workflows.
Data Engineering & Analytics Services
Enterprise data platforms built as scoped workstreams on the Scrums.com platform: lakehouse architecture, real-time data pipelines, governance and quality, BI and ML-ready infrastructure. Scrums.com owns the outcome, from siloed data and slow analytics to one governed platform your analysts and models trust.
Delivering since 2012 · Trusted by 400+ enterprises · 40-60% lower cost than traditional data warehousing
- Lakehouse platforms
- Real-time pipelines
- Governance
- BI and MLOps
80%
Fewer data incidents
40-60%
Lower cost than separate warehouses and lakes
30-50%
Lower cloud data costs
4-6 wks
First analytics use case live
< 21 days
Data workstream live
01
Data engineering scopes you can deploy in plan
§ data / scopesFixed-scope work from the scopes register. Milestone-based, SLA-backed and run on the platform. Building AI on the data? See AI development.
Outcome-driven sprintData Pipeline FoundationA working ELT baseline for your business: source ingestion, a governed warehouse, and your first dashboards — built on dbt, Airflow, and your cloud, with data quality checks from the first pipeline.View scope Outcome-driven sprintCloud Data Warehouse ImplementationCreate a structured cloud warehouse with a tested transformation layer and modeled datasets ready for analytics and BI. Finish state: governed, documented datasets your analysts actually query.View scope PodData & Analytics PodOngoing data engineering and analytics capacity working a metrics backlog: pipeline build and operations, self-service BI, AI-powered data quality, and ML-ready infrastructure — delivered as a standing pod.View scope
02
AI-native data engineering on the Scrums.com platform
§ data / aiData engineering on Scrums.com is delivered as scoped workstreams: the data platform, the data pipelines, the governance layer and the analytics on top, built to milestones on the SEOP platform with Scrums.com accountable for the outcome. Data platforms are built as products, combining lakehouse architecture, DataOps practices and data mesh principles into scalable, self-service, governed infrastructure that evolves with your organisation.
DataOps and pipeline automation
DevOps practices applied to data: version-controlled transformations, automated testing, CI/CD for data workflows and observability. Every pipeline change is reviewed, tested in staging and deployed automatically.
AI-powered data quality
Agents monitor pipelines for anomalies, schema drift, quality issues and performance degradation, predict failures and remediate common issues, cutting mean time to detection from hours to minutes.
03
Data engineering and analytics since 2012
§ data / company2012Delivering since
400+Enterprises trust the platform
40-60%Lower cost than traditional data warehousing
3-5xFaster with proven lakehouse patterns
Unlike data consultancies focused on expensive enterprise tools, Scrums.com delivers modern lakehouse-based platforms on cost-effective technologies: Databricks, open-source tools and cloud-native services. Pre-built architecture templates accelerate implementation, and ML readiness is built in from day one, not added afterwards.
04
Data platforms, pipelines, governance, ML and analytics, one workstream
§ data / areasEnterprise data platform architecture
Modern data platforms designed and implemented on Databricks Lakehouse, Snowflake or cloud-native data services: warehouses, data lakes and streaming infrastructure consolidated into one platform for SQL analytics, Python/Spark processing and ML workloads. Data silos eliminated, infrastructure simplified, time-to-insight shortened.
- Lakehouse architecture consolidating warehouses, lakes and streaming
- SQL analytics, Python/Spark and ML on a single platform
- Warehouse migrations from Oracle, Teradata and SQL Server
- Scalable, cost-effective architecture
Real-time data pipelines and streaming
Real-time data pipelines on Kafka, Spark Streaming, Flink or cloud-native streaming services, processing millions of events per second: change data capture, stream processing, real-time aggregations and streaming analytics that turn batch-oriented reporting into continuous insight.
- Change data capture from transactional systems
- Stream processing and real-time aggregations
- Real-time dashboards and operational analytics
- Instant alerting on business-critical metrics
ETL/ELT pipeline development and orchestration
Scalable ETL/ELT pipelines that extract data from diverse sources, transform it for analytics and load it into target platforms, with incremental processing, dependency management, error handling and monitoring, so data moves from transactional systems to analytics reliably.
- Databases, APIs, SaaS tools, event streams and files integrated
- Incremental processing and dependency management
- Pipeline CI/CD with tests on every change
- Failures alerted immediately with automated diagnostics
Data governance and data quality
Data governance established with data catalogs, lineage tracking, quality monitoring, access controls and compliance frameworks for GDPR, CCPA and SOC 2. Automated quality checks, anomaly detection, schema validation and data observability keep the data behind analytics and ML trusted, reducing data incidents by 80%.
- Data catalogs, lineage tracking and access controls
- Automated quality checks and schema validation
- AI anomaly detection and data observability
- Compliance frameworks for regulated data
ML/AI infrastructure and MLOps
ML-ready data infrastructure: feature stores, model training pipelines, experiment tracking, model serving and monitoring. MLOps practices on MLflow, SageMaker or Databricks ML let data scientists train, deploy and monitor models in production, accelerating ML development from months to weeks.
- Feature stores and model training pipelines
- Experiment tracking, model serving and monitoring
- The data foundations RAG and fine-tuning depend on
- Standardised infrastructure and automated workflows
Data analytics services: BI and self-service analytics
Analytics infrastructure and business intelligence tools connected to governed data platforms: semantic layers, data marts and reusable metrics, with self-service analytics that let business users generate insights without data team bottlenecks.
- Semantic layers, data marts and reusable metrics
- BI tool deployment and configuration
- Self-service analytics for business users
- Organisation-wide, data-driven decision making
Every area is a scope
Data scopes register Each capability above is delivered as a fixed-scope workstream from the scopes register: agreed acceptance criteria, milestones, evidence you can stand behind, and no timesheets.
05
Data analytics consulting: from landscape assessment to ML-ready platforms
§ data / runEvery engagement starts with data analytics consulting: infrastructure, sources, use cases, data quality and governance maturity assessed, then a phased roadmap with success metrics. Value lands incrementally. The first analytics use cases go live within 4-6 weeks, a basic platform in 1-2 months, a standard lakehouse in 3-4 months and an enterprise platform in 6-12 months.
Process Four phases
- Data landscape assessment and strategyCurrent infrastructure and tooling assessed, data sources inventoried, stakeholders interviewed for analytics and ML use cases, data quality and governance maturity evaluated, target architecture designed and a prioritised roadmap set with success metrics.
- Platform foundation and initial pipelinesData platform provisioned, storage and orchestration set up, governance tooling configured, initial pipelines built for priority sources with a transformation framework, quality validation and monitoring, and the first analytics use case delivered.
- Pipeline expansion and analytics enablementRemaining sources integrated, real-time streaming pipelines implemented, historical data backfilled, semantic layer and metrics framework created, BI tools deployed and self-service analytics established, with users trained.
- ML infrastructure and advanced capabilitiesFeature stores, training pipelines and model serving deployed, MLOps platform in place, advanced analytics implemented, data mesh patterns for large organisations where needed, and cost and performance continuously optimised.
Stack Technologies we use
- PlatformsDatabricks, Snowflake and cloud-native data services on AWS, Azure and GCP; lakehouse storage on object stores; warehouse migrations from Oracle, Teradata and SQL Server.
- Pipelines and streamingKafka, Spark Streaming and Flink for real-time data; change data capture from PostgreSQL, MySQL, SQL Server, Oracle and MongoDB; APIs, webhooks and file systems.
- Transformation and orchestrationdbt and Spark for transformations; Airflow, Prefect and Databricks Workflows for orchestration; version-controlled pipelines with automated tests.
- Analytics and MLTableau, Power BI, Looker and Metabase for BI; MLflow, SageMaker and Databricks ML for feature stores, experiment tracking and model serving.
06
Data warehouse, data lake or lakehouse
§ data / architectureData warehouse
Snowflake · BigQuery · RedshiftStores structured data optimised for SQL analytics. Fast for business intelligence, but expensive, limited to structured data and not ML-friendly.
Data lake
S3 · ADLSStores any data type cheaply, but needs complex engineering for analytics: flexible, with poor performance and governance challenges.
Lakehouse
Databricks · Delta LakeCombines both: cheap storage like a lake and fast SQL analytics like a warehouse, for structured and unstructured data with ML integration. Recommended for modern platforms: 40-60% cheaper than warehouses and more performant than lakes.
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When data engineering workstreams make sense
§ data / fitIt makes sense when
Data is siloed across systems
Consolidating for analytics needs manual exports, complex integrations or unreliable processes. Unified platforms eliminate the silos.
Analytics takes days or weeks
Pipelines are batch-oriented, unreliable or need manual intervention. Real-time pipelines deliver insight as events happen.
Data quality issues erode trust
Stakeholders question the numbers and reports disagree. Automated quality checks and lineage restore confidence.
Your organisation lacks ML/AI infrastructure
Data scientists spend 80% of their time wrangling data instead of building models. ML-ready platforms accelerate AI.
Business users cannot self-serve analytics
Every report, dashboard or data question waits on the data team. Governed self-service removes the bottleneck.
Consider alternatives when
You need AI or ML models built, not only the data under them
LLM products, RAG systems, agents and fine-tuning are AI development workstreams. AI Development →
You need DevOps for the platform itself
CI/CD, infrastructure-as-code and Kubernetes for data workloads run as DevOps workstreams. DevOps Engineering →
You are building analytics into your own product
Customer-facing charts, metrics and reports with tenant-safe data access are a scoped feature. Embedded Analytics Feature →
You need one data engineer in a team you direct
Add a data engineer, data architect or ML engineer from the register to your existing data team. Hire data engineers →
08
Data engineering and analytics FAQs
§ data / faqHow long does data platform implementation take?
A basic platform (single data warehouse, batch pipelines, basic reporting) deploys in 1-2 months. A standard lakehouse (real-time pipelines, BI tools, governance) takes 3-4 months. An enterprise platform (comprehensive pipelines, ML infrastructure, data mesh) takes 6-12 months. Delivery is incremental: the first analytics use cases go live within 4-6 weeks while the platform builds progressively.
Can you migrate our existing data warehouse to a modern lakehouse?
Yes. Data platform modernization migrates traditional warehouses (Oracle, Teradata, SQL Server) to modern lakehouses (Databricks, Snowflake). Strategies include a parallel run (build the lakehouse alongside the warehouse and migrate workloads gradually), a phased migration of pipelines and reports, or a hybrid approach. Most migrations complete in 6-12 months with zero downtime for critical analytics.
What if our data is currently in spreadsheets and manual processes?
That is a good starting point. Source system integration replaces manual exports with automated pipelines, spreadsheet data is unified into a governed platform, business users get BI tools instead of spreadsheets, and migration starts with critical reports. Within 2-3 months, most critical spreadsheet-based analytics move to automated, reliable data platforms.
How do you ensure data quality and reliability?
A data quality framework with schema validation, automated checks for completeness, accuracy and uniqueness, AI anomaly detection, data lineage from source to analytics, pipeline monitoring that alerts on failures and performance degradation, and data observability. This reduces data quality incidents by 80% and mean time to detection from hours to minutes.
Can data platforms integrate with our existing tools and systems?
Yes: databases (PostgreSQL, MySQL, SQL Server, Oracle, MongoDB), SaaS applications (Salesforce, HubSpot, Stripe, Google Analytics), event streams (Kafka, Kinesis, Pub/Sub), APIs and webhooks, file systems (S3, Azure Blob, SFTP), legacy systems and custom applications. Modern data platforms excel at heterogeneous source integration.
Do we need dedicated data engineers in-house?
Not initially. The workstream brings the expertise and progressively trains your engineers through hands-on collaboration and knowledge transfer. Long-term platform success benefits from internal data engineering capability, either hiring data engineers or upskilling software engineers into platform roles; both a fully managed platform and capability building for self-sufficiency are supported.
What is the difference between a data engineering workstream and hiring a data team?
Hiring in-house takes 3-6 months of recruitment at $150K-$250K a year per engineer, plus training, tool licensing and the risk of losing expertise to turnover. A data engineering workstream deploys in under 3 weeks, costs 40-60% less than US and UK hires, brings proven platform expertise across pipelines, ML and governance, and scales with your needs.
How do you measure data platform success?
Through SEOP dashboards: time to insight, data quality (completeness, accuracy and freshness scores), pipeline reliability (uptime, failure rate, MTTR), self-service adoption, ML training time, cloud costs per query and storage, and internal user satisfaction. Quarterly reviews demonstrate return through faster analytics, less data team toil and ML acceleration.
What happens after the initial data platform implementation?
Ongoing platform engineering that keeps expanding pipelines, adding sources and optimising performance; managed data operations with pipeline monitoring, incident response and maintenance; advisory with a part-time data architect guiding platform evolution; or knowledge transfer and handoff for complete platform ownership. Most clients choose managed operations or advisory to keep platform momentum.
DATA ENGINEERING · SORTED
Data your teams trust, ready for analytics and AI.
Data engineering services as scoped workstreams on the Scrums.com platform: lakehouse platforms, real-time pipelines, governance, BI and ML-ready infrastructure, with Scrums.com accountable for the outcome.
PLATFORMS · PIPELINES · GOVERNANCE · ANALYTICS · MLOPS