Big Data Services — From Ingestion to Insight
Data pipelines break at 3 AM, dashboards show stale numbers, and your data team spends 80% of their time fixing infrastructure instead of building models. Opsio's big data services engineer production-grade data platforms on Spark, Kafka, Databricks, and Snowflake so your data actually flows reliably from source to insight.
Trusted by 100+ organisations across 6 countries
Spark
& Databricks
Kafka
Streaming
PB-Scale
Data Platforms
Real-Time
Pipelines
What's Included
Data Lakehouse Architecture
Databricks with Delta Lake or Apache Iceberg on S3, ADLS, or GCS. ACID transactions, time travel, schema evolution, Z-ordering optimization, and unified batch and streaming processing. We eliminate the dual lake-warehouse architecture that doubles infrastructure costs and complexity.
Real-Time Streaming Pipelines
Apache Kafka and Confluent for event streaming with schema registry, exactly-once semantics, and consumer group management. Spark Structured Streaming, Flink, or Kafka Streams for real-time transformations with windowed aggregations, late data handling, and watermark management.
Pipeline Orchestration
Apache Airflow or Dagster for workflow orchestration with dependency management, retry logic, SLA monitoring, and alerting. We build modular DAGs with proper error handling, data lineage tracking, and integration testing. Pipelines are version-controlled and deployed through CI/CD.
Data Quality & Contracts
Great Expectations, dbt tests, or Monte Carlo for automated validation: schema checks, freshness monitoring, volume anomaly detection, and distribution analysis. Data contracts between producers and consumers prevent upstream schema changes from silently breaking downstream systems.
dbt Transformation Layer
dbt models for SQL-based transformations with incremental materialization, snapshots for slowly changing dimensions, macros for reusable logic, and comprehensive testing. We build modular dbt projects with clear documentation that data analysts can extend independently.
Data Platform Cost Optimization
Spark cluster autoscaling and right-sizing, Snowflake warehouse auto-suspend and auto-scale configuration, Delta Lake OPTIMIZE and VACUUM for storage efficiency, and spot instances for batch workloads. We typically reduce data platform costs by 30-50% while improving performance.
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Our AWS migration has been a journey that started many years ago, resulting in the consolidation of all our products and services in the cloud. Opsio, our AWS Migration Partner, has been instrumental in helping us assess, mobilize, and migrate to the platform, and we're incredibly grateful for their support at every step.
Roxana Diaconescu
CTO · SilverRail Technologies
Data Platforms That Deliver Reliable Insights
Most data platforms grow organically — a Kafka cluster here, a Spark job there, a tangled web of Airflow DAGs that nobody fully understands. The result is fragile pipelines that break when source schemas change, data quality issues that propagate silently to dashboards, and a data engineering team that is permanently firefighting instead of building new capabilities. Opsio's big data services bring engineering discipline to your data platform. We design data lakehouse architectures on Databricks with Delta Lake, Snowflake for cloud data warehousing (see our Snowflake architecture layers guide), Apache Spark for distributed processing, Apache Kafka and Confluent for real-time streaming, and Apache Airflow or Dagster for pipeline orchestration — all with proper testing, monitoring, and data quality frameworks. Where the underlying database itself needs to move, we coordinate with our database migration service for source-to-target cutovers.
Real-time streaming architectures are where most organizations struggle. We implement Kafka-based event streaming pipelines with schema registry, exactly-once processing semantics, and consumer group management. For teams that need real-time analytics, we configure Spark Structured Streaming, Flink, or Kafka Streams with windowed aggregations and watermark handling.
Data quality is not optional — it is the foundation of trust. We implement Great Expectations, dbt tests, or Monte Carlo for automated data validation at every pipeline stage. Schema enforcement, freshness monitoring, volume anomaly detection, and distribution checks catch issues before they reach dashboards. Data contracts between producers and consumers prevent upstream changes from breaking downstream systems.
The data lakehouse pattern combines the flexibility of data lakes with the reliability of data warehouses. We build lakehouse architectures on Databricks with Delta Lake or Apache Iceberg, implementing ACID transactions, time travel, schema evolution, and Z-ordering for query optimization. This eliminates the need for separate data lake and warehouse systems.
Cost optimization for big data requires understanding both compute and storage patterns. We right-size Spark clusters with autoscaling, configure Snowflake warehouse suspension policies, implement Delta Lake OPTIMIZE and VACUUM for storage efficiency, and use spot instances for batch workloads — and report the savings back through a dashboard pattern we documented in our cost optimization dashboard guide. Clients typically reduce data platform costs by 30-50% while improving pipeline reliability. For BigQuery-heavy estates we coordinate with our Google Cloud Platform practice on slot reservations, and Azure-side data services are handled by our Azure managed services team. Related Opsio services: Google Cloud Platform (GCP) — Data & AI Cloud, Serverless Services — Scale Without Servers, and Kubernetes Consulting — Tame Container Complexity.
How Opsio Compares
| Capability | In-House Team | Other Provider | Opsio |
|---|---|---|---|
| Lakehouse architecture | Separate lake and warehouse | Basic Delta Lake | Production lakehouse with Iceberg/Delta |
| Streaming pipelines | Batch only | Basic Kafka setup | Kafka with schema registry and exactly-once |
| Data quality | Manual spot checks | Basic dbt tests | Great Expectations + contracts + monitoring |
| Pipeline reliability | Break-fix reactive | Basic alerting | SLA monitoring with automated retry and alerting |
| Cost optimization | Over-provisioned clusters | Occasional review | Autoscaling + spot + 30-50% savings |
| Orchestration maturity | Cron jobs | Basic Airflow | Production Airflow/Dagster with CI/CD |
| Typical annual cost | $350K+ (2-3 data engineers) | $150-250K | $72-216K (fully managed) |
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What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
Data Platform Assessment
$10,000–$25,000
1-2 week engagement
Platform Build & Migration
$40,000–$120,000
Most popular — full implementation
Managed Data Platform Ops
$6,000–$18,000/mo
Ongoing operations
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
Production data engineering
Spark, Kafka, Databricks, and Snowflake platforms running reliably at petabyte scale.
Real-time streaming experts
Kafka event pipelines with exactly-once semantics and schema registry.
Data quality built in
Great Expectations and dbt tests catching issues before they reach dashboards.
Lakehouse architecture
Delta Lake and Iceberg unifying batch and streaming in one platform.
Cost optimization included
30-50% data platform cost reduction through compute and storage optimization.
Pipeline reliability focus
SLA monitoring, alerting, and automated retry ensuring data arrives on time.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our 4-Phase Delivery Process
Data Platform Assessment
Audit existing data infrastructure, pipeline reliability, data quality, and team capabilities. Deliverable: data platform maturity scorecard and prioritized roadmap. Timeline: 1-2 weeks.
Architecture Design
Design target data platform: lakehouse architecture, streaming pipelines, orchestration layer, data quality framework, and governance model. Select technology stack. Timeline: 2-3 weeks.
Build & Migrate
Implement data platform components, migrate existing pipelines, configure monitoring and alerting, and deploy data quality checks across all pipeline stages. Timeline: 6-12 weeks.
Operate & Scale
Ongoing pipeline monitoring, incident response, cost optimization, capacity planning, new pipeline development support, and quarterly platform reviews. Timeline: Ongoing.
Key Takeaways
- Data Lakehouse Architecture
- Real-Time Streaming Pipelines
- Pipeline Orchestration
- Data Quality & Contracts
- dbt Transformation Layer
Industries Served by Opsio
Financial Services
Transaction analytics, risk modeling, and regulatory reporting pipelines.
E-commerce & Retail
Customer behavior analytics, recommendation engines, and demand forecasting.
Healthcare & Pharma
Clinical data pipelines, patient analytics, and regulatory compliance reporting.
Manufacturing & Logistics
IoT sensor data processing, supply chain analytics, and predictive maintenance.
Big Data Services — From Ingestion to Insight — FAQ
What are big data services and what do they include?
Big data services cover the design, implementation, and operation of data platforms that handle large-scale data processing — from ingestion and streaming through transformation, storage, and analytics. Opsio's services include data lakehouse architecture on Databricks or Snowflake, real-time streaming with Kafka, pipeline orchestration with Airflow, data quality with Great Expectations, and ongoing platform operations. For example, we help retail companies ingest millions of daily transactions, transform them into analytical models with dbt, and surface insights through real-time dashboards. Our platform engineers manage the underlying infrastructure so your data team focuses on generating business value rather than troubleshooting cluster issues.
What is a data lakehouse and why should I use one?
A data lakehouse combines the flexibility of a data lake with the reliability of a data warehouse using Delta Lake or Apache Iceberg on object storage. You get ACID transactions, schema enforcement, time travel, and SQL query performance — without maintaining separate lake and warehouse systems. This reduces infrastructure cost and complexity while providing a single source of truth for analytics and ML workloads. Time travel allows analysts to query data as it existed at any historical point, which is invaluable for debugging data issues and reproducing past reports accurately.
How much do big data services cost?
A data platform assessment runs $10,000-$25,000. Architecture design and implementation ranges from $40,000-$120,000 depending on complexity and number of data sources. Managed data platform operations cost $6,000-$18,000 per month. Most clients see ROI through improved data reliability, reducing business decisions based on stale data, and achieving 30-50% infrastructure cost savings. For example, a mid-size company with 20 data sources and five terabytes of data typically invests $80,000 in platform implementation and $10,000 monthly for managed operations. Cluster optimization and intelligent auto-scaling usually save $5,000-$8,000 monthly on compute costs, making the management investment largely self-funding.
How does Opsio handle real-time data streaming?
We implement Apache Kafka or Confluent for event streaming with schema registry for data governance, exactly-once processing semantics for data accuracy, and consumer group management for scalable consumption. For real-time analytics, we configure Spark Structured Streaming, Flink, or Kafka Streams with windowed aggregations and late data handling. For example, an e-commerce platform might stream clickstream data through Kafka, process it with Spark Structured Streaming for real-time recommendation updates, and simultaneously persist events to the data lakehouse for batch analytics. Schema registry ensures that upstream schema changes do not break downstream consumers by enforcing backward compatibility rules automatically.
What data quality tools does Opsio implement?
We use Great Expectations for pipeline validation, dbt tests for transformation layer quality, and Monte Carlo for data observability. Automated checks cover schema validation, freshness monitoring, volume anomaly detection, and distribution analysis. Data contracts between producers and consumers prevent upstream changes from breaking downstream systems without notification. For example, a data contract might specify that the orders table must contain at least 10,000 new rows daily, that the revenue column cannot contain null values, and that the distribution of order values must remain within two standard deviations of the historical average.
Can Opsio migrate from legacy ETL tools to modern data platforms?
Yes. We migrate from legacy ETL tools like Informatica, Talend, SSIS, and custom scripts to modern platforms. The process includes pipeline analysis, dependency mapping, incremental migration with parallel running, validation testing, and decommissioning. We typically use Airflow or Dagster for orchestration and dbt for transformations in the target architecture. For example, we recently migrated 150 SSIS packages to dbt models running on Snowflake, reducing data processing time from 8 hours to 45 minutes. The migration ran in parallel for six weeks, with automated validation comparing output between legacy and modern pipelines row-by-row to ensure data consistency before cutover.
What is the difference between Databricks and Snowflake?
Databricks excels at large-scale data engineering with Spark, ML workloads, and Delta Lake lakehouse architecture. Snowflake leads in ease-of-use for SQL analytics with near-zero administration and instant scaling. Many organizations use both — Databricks for data engineering and ML, Snowflake for BI and ad-hoc analytics. We recommend based on workload mix and team skills. For example, a company with heavy Python-based data science and large-scale ETL pipelines would benefit from Databricks, while a team of SQL analysts needing fast ad-hoc queries would prefer Snowflake. Opsio implements and manages both platforms, often configuring data sharing between them for comprehensive analytics coverage.
How does Opsio ensure data pipeline reliability?
We implement SLA monitoring for pipeline completion times, automated alerting for failures and data quality violations, retry logic with exponential backoff, dead-letter queues for failed records, and circuit breakers for downstream dependencies. Every pipeline has documented runbooks for common failure scenarios and is monitored 24/7 by our operations team. For example, if a daily pipeline must complete by 6 AM for morning dashboards, we set SLA alerts at 5 AM and escalate if processing is still running. Dead-letter queues capture failed records for investigation without blocking the overall pipeline.
What pipeline orchestration tools does Opsio use?
We primarily use Apache Airflow for its broad integration ecosystem and community support, and Dagster for teams that prefer a more modern, asset-based orchestration model. Both tools are deployed with proper monitoring, CI/CD for DAG deployments, and testing frameworks. We also support Prefect and Databricks Workflows depending on your existing platform. Airflow excels for teams with complex scheduling requirements and extensive third-party integrations. Dagster provides better developer experience with type-checked inputs and outputs, built-in data lineage, and software-defined assets. We help you evaluate both options based on your needs.
How does Opsio optimize data platform costs?
We combine multiple strategies: Spark cluster autoscaling and spot instance usage for batch jobs, Snowflake warehouse auto-suspend and resource monitor configuration, Delta Lake OPTIMIZE and VACUUM for storage efficiency, partition pruning for query cost reduction, and data lifecycle policies for archival. Monthly cost reports track savings and identify new optimization opportunities. For example, configuring Snowflake warehouses to auto-suspend after 60 seconds of inactivity and using appropriately sized warehouses per workload type typically reduces compute costs by 30-40%. For Spark workloads, we leverage spot instances for batch processing and configure cluster autoscaling to match actual demand rather than peak provisioning.
Still have questions? Our team is ready to help.
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Big Data Services — From Ingestion to Insight
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