Connect fragmented ERPs, CRMs, APIs, IoT sensors, and transactional databases into self-healing, automated data pipelines built on Apache Spark, Delta Lake, and Microsoft Fabric.
When executives question dashboard numbers, the root cause is rarely the visualization. It is almost always upstream: brittle scripts that silently fail, unhandled schema drift, delayed night batches, and unverified data logic.
At DAVIZ Solutions, we treat data engineering as a mission-critical software engineering discipline. We build resilient pipelines with automated data testing, schema contracts, idempotent ingestion, and comprehensive observability so your business operations run on trusted facts.
Ingest high-frequency transactional data from SQL Server, Oracle, SAP, and Postgres directly from transaction logs without impacting source production system performance or CPU.
Enforce data contracts at ingestion. Invalid records, missing foreign keys, and corrupted rows are automatically routed to quarantine tables while clean data flows to Gold reporting uninterrupted.
Separate raw landing data (Bronze) from conformed business entities (Silver) and curated dimensional models (Gold) using Delta Lake open Parquet storage with time-travel and rollback capabilities.
Process multi-terabyte datasets in minutes. We write optimized PySpark notebooks, configure dynamic executor allocation, and apply liquid clustering and V-Order indexing for lightning-fast reads.
Track pipeline freshness SLAs, execution durations, compute costs, and schema drift with automated Microsoft Teams and email alerts before business stakeholders ever detect a delay.
Our certified engineers implement production-grade frameworks designed to withstand real-world operational chaos and API volatility.
Modern data pipelines cannot rely on nightly full-table dumps that overload transactional databases. We implement low-latency log-based CDC across Microsoft SQL Server, Oracle, SAP ERP, Salesforce, and Dynamics 365. Incremental changes (inserts, updates, deletes) are synchronized continually with zero operational impact on your core business systems.
When dataset volumes exceed billions of rows, traditional single-node ETL tools break down. Our teams build scalable, distributed data processing jobs using Apache Spark and PySpark. We implement modular notebook frameworks, automated unit tests, reusable helper libraries, and Git-controlled code versioning for seamless team collaboration.
We architect strict Medallion separation to ensure data integrity and full auditability. Bronze preserves raw, immutable source history. Silver standardizes, cleanses, deduplicates, and resolves master customer/vendor identities. Gold builds star-schema dimensional models optimized for sub-second Direct Lake Power BI queries.
Never let corrupted data pollute executive reports. We build proactive data quality gates into every transformation pipeline. Using schema enforcement, uniqueness checks, referential integrity validation, and statistical anomaly detection, bad records are automatically diverted into isolated quarantine tables with automated alert notifications.
For logistics fleets, manufacturing sensors, financial fraud detection, and point-of-sale transactions, batch data is often too late. We build real-time event streaming pipelines using Azure Event Hubs, Kafka, and Microsoft Fabric Eventstreams. Telemetry is parsed, aggregated, and stored in KQL databases for live operational dashboards.
Maintain total operational control over your data estate. We implement centralized observability dashboards that track pipeline run times, failure rates, data freshness SLAs, and cloud compute expenditures. Automated anomaly alerts notify your engineers before downstream business operations are impacted.
Understand why leading enterprises are moving away from brittle, black-box SQL scripts to testable, scalable, and version-controlled data engineering frameworks.
| Pipeline Dimension | Legacy Stored Procedure ETL (SSIS / SQL Scripts) | Modern Medallion Lakehouse (Spark / Fabric) |
|---|---|---|
| Failure Recovery | ✕ Manual Rollback — Crashes leave dirty data; requires manual developer intervention | ✓ ACID Transactions — Atomic commits guarantee all-or-nothing data consistency |
| Data Testing | ✕ Post-Mortem — Discovered by end users when executive reports show incorrect numbers | ✓ In-Flight Validation — Automated schema and contract tests quarantine bad rows upfront |
| Scalability | ✕ Single-Node Limit — Maxes out database CPU and memory on heavy data volumes | ✓ Distributed Compute — Scales dynamically across Spark clusters or Fabric F-SKUs |
| Version Control | ✕ Untracked Changes — Code edited directly in database; difficult to review or audit | ✓ Full Git CI/CD — Pull request reviews, automated linting, and promotion environments |
| Historical Auditing | ✕ Overwritten Data — Updates overwrite historical states unless complex SCD2 is built | ✓ Delta Time Travel — Query any previous table version or point in time instantly |
| Downstream BI | ✕ Slow Extracts — Power BI must extract data into separate proprietary memory models | ✓ Direct Lake Ready — Power BI reads directly from open Delta files at in-memory speed |
A disciplined engineering workflow that delivers production reliability from day one.
We conduct an exhaustive audit of your data sources—ERPs, transactional databases, CRM APIs, flat files, and streaming endpoints. We profile data volumes, cardinality, null ratios, and identify upstream bottlenecks.
Microsoft Fabric
Power BI
Microsoft Azure
S/4HANA
Fusion Cloud
Salesforce
Snowflake
Databricks
Dynamics 365From diagnosing pipeline issues to building modern data platforms and providing dedicated engineering expertise.
Find the root cause of pipeline problems.
Build reliable production-ready data pipelines.
Extend your engineering capabilities.
Connect with our senior data engineers in Dubai. We will review your current pipeline architecture, diagnose your latency bottlenecks, and deliver a clear plan.
Traditional batch pipelines execute large queries (e.g.
SELECT * WHERE updated_at > yesterday) which scan entire database tables, locking rows,
causing query timeouts, and straining CPU on production ERPs. Change Data Capture (CDC)
reads directly from the database transaction log (like SQL Server transaction log or Oracle redo log). It
captures every row insert, update, and delete in near real time with virtually zero CPU overhead, enabling
sub-5-minute data freshness without impacting operational systems.
The Medallion architecture enforces strict quality gates across three tiers:
• Bronze (Raw): Exact, immutable mirror of source data with metadata audit stamps.
• Silver (Cleansed): Cleansed, deduplicated, and conformed enterprise data with
schema validation and business rule checks. Records failing validation are automatically routed to
quarantine.
• Gold (Business-Ready): Star-schema dimensional marts and aggregated KPIs certified
for executive Power BI consumption and machine learning.
Yes. Legacy SSIS packages and nested SQL stored procedures are notorious for lack of version control, difficult debugging, and poor horizontal scalability. We reverse-engineer your existing business logic, map out source-to-target data dependencies, and rebuild the workflows as parameterized, testable PySpark notebooks or Fabric Dataflow Gen2 pipelines with full Git CI/CD integration.
Schema drift occurs when source application updates alter column types, rename fields, or add new columns without warning. We build automated schema contracts into ingestion pipelines using Delta Lake schema evolution and Great Expectations. When a critical schema violation occurs, the pipeline catches the drift, logs an alert, quarantines the affected payload, and prevents downstream model corruption.
We apply several architectural best practices:
1. Liquid Clustering & Z-Order: Organizes Parquet files along high-cardinality
filter columns to maximize file skipping during query execution.
2. Delta V-Order: Microsoft Fabric optimization that applies dictionary and
run-length encoding for in-memory read speeds matching VertiPaq.
3. Auto-Compaction: Automatically merges small 10MB files into optimal 128MB–1GB
Parquet files to eliminate the small-file overhead.
For ongoing managed pipeline engagements, we provide a structured Service Level Agreement (SLA) with guaranteed 15-minute emergency incident response times for critical production pipelines, 24/7 automated telemetry monitoring, monthly capacity FinOps reviews, and dedicated senior data engineering leads.
