How do you handle schema drift in high-volume streaming pipelines?

We implement Confluent Schema Registry and Avro/Protobuf protocols. Any upstream change that violates backward compatibility is quarantined in a Dead Letter Queue (DLQ) with automated alerting, preventing corrupted records from polluting downstream tables.

Can Synora Soft migrate our legacy on-premise Hadoop / SQL Server to Databricks or Snowflake?

Yes. We have structured migration accelerators that convert legacy Hive, Spark, and SQL Server stored procedures into optimized PySpark and dbt models, validating parity before cutting over live production pipelines with zero operational downtime.

How does your data platform integrate with our machine learning and AI initiatives?

Our lakehouses expose clean Feature Stores and direct lakehouse data shares directly to ML and AI platforms (such as Databricks MosaicML, SageMaker, and Azure ML). We also integrate vector database syncs that keep semantic indices continuously refreshed.

Data Lakehouses & Real-Time Streaming

Modern Data Platforms for Real-Time Intelligence

We architect unified modern data lakehouses, high-throughput Apache Kafka streaming engines, automated dbt transformation pipelines, and rigorous data governance frameworks that feed operational AI and executive decision-making.

Schedule Data Strategy Session Explore Capabilities

Data Debt & Silos

Critical Enterprise Data Bottlenecks We Eliminate

Stale batch ETLs, duplicated data stores, and untrusted metrics handicap analytics and sabotage AI reliability.

Challenge 01

Batch Latency & Stale Operational Intelligence

Overnight 24-hour batch ETL runs prevent real-time fraud detection, dynamic pricing, and immediate equipment failure intervention.

Business Impact:Delayed executive decision-making and missed operational intervention windows.

Challenge 02

Warehouse & Lakehouse Fragmentation

Data duplicated between disparate object stores and proprietary data warehouses leads to spiraling cloud storage bills and irreconcilable metrics.

Business Impact:Up to 40% excess storage and compute spend from redundant ingestion pipelines.

Challenge 03

Poor Data Quality & Lack of Lineage

Data pipelines break silently when upstream application schemas change, polluting downstream dashboards and AI training sets with bad data.

Business Impact:Executive distrust in BI reporting and contaminated machine learning model predictions.

Challenge 04

Governance Chaos Under Stricter Compliance

Managing PII, HIPAA, and GDPR access controls across hundreds of disparate database tables without unified automated policy enforcement.

Business Impact:Immense audit vulnerability and threat of substantial regulatory non-compliance fines.

Engineered for Velocity

Enterprise Data Capabilities

Architectural blueprints designed for sub-second streaming, zero vendor lock-in, and unified governance.

Lakehouse CoreLakehouse Standard

Unified Modern Lakehouse Architecture

Consolidate structured, semi-structured, and unstructured data on open table formats (Apache Iceberg, Delta Lake) with Databricks and Snowflake.

Architectural Deliverables:

Medallion architecture implementation (Bronze ingestion, Silver enrichment, Gold aggregates)

Open table formats ensuring zero proprietary vendor lock-in

Decoupled compute and storage scaling with automated workload cluster suspension

Direct integration with enterprise AI frameworks and vector search indices

APACHE ICEBERG / DELTADBT & KAFKA NATIVE

Sub-Second LatencyLakehouse Standard

Real-Time Event Streaming & Ingestion

Build distributed streaming pipelines using Apache Kafka, Apache Flink, and AWS Kinesis processing millions of sensor, clickstream, and transactional events per second.

Architectural Deliverables:

High-throughput schema-governed Kafka clusters with Schema Registry enforcement

Stateful stream transformations and event windowing via Apache Flink

Change Data Capture (CDC) using Debezium directly from operational OLTP databases

Immediate sub-second synchronization into analytical data stores and telemetry engines

APACHE ICEBERG / DELTADBT & KAFKA NATIVE

Versioned AnalyticsLakehouse Standard

Analytics Engineering & Data Mesh with dbt

Apply software engineering best practices to data transformations using dbt (data build tool), Git version control, continuous integration, and automated schema testing.

Architectural Deliverables:

Modular, reusable SQL/Python transformation models with complete DAG visualization

Automated unit testing and anomaly assertions on every ingestion batch

Data Mesh domain-oriented data product modeling with clear decentralized ownership

Automated interactive documentation and column-level data lineage tracking

APACHE ICEBERG / DELTADBT & KAFKA NATIVE

Operational ActivationLakehouse Standard

Automated Data Governance & Reverse ETL

Implement centralized data access policies, automated PII masking, and Reverse ETL pipelines syncing trusted warehouse data back into operational tools like Salesforce and ServiceNow.

Architectural Deliverables:

Granular role-based access control (RBAC) and dynamic row/column-level data masking

Unity Catalog and Snowflake Horizon centralized governance and audit trails

Reverse ETL syncing golden customer metrics directly into Salesforce and HubSpot

Automated compliance reporting satisfying SOC 2, HIPAA, and GDPR standards

APACHE ICEBERG / DELTADBT & KAFKA NATIVE

Data Ecosystem Alliances

Modern Data Stack & Cloud Lakehouses

We build on open, resilient, high-throughput technologies.

Data

Databricks Lakehouse

Services Provider

Data

Snowflake Cloud

Services Provider

Data

Apache Kafka / Confluent

Streaming Engine

Data

dbt (Data Build Tool)

Analytics Engineering

Data

Apache Iceberg

Open Table Format

Data

Delta Lake

ACID Transactions

Cloud

AWS Redshift / EMR

Cloud Big Data

Cloud

Azure Synapse & Fabric

Enterprise Analytics

Data

Fivetran / Airbyte

Automated Ingestion

Data

Unity Catalog

Centralized Governance

Measurable Platform ROI

Quantified Business Outcomes from Modern Data Architecture

Accelerate query performance, slash infrastructure spend, and trust your data.

Query Latency Reduction

Achieved via Delta Lake and Snowflake clustering optimization.

Sub-Sec

Event Ingestion Speed

Real-time Apache Kafka streaming replacing 24-hour legacy batch windows.

Cloud Spend Savings

Eliminating duplicate pipelines and optimizing auto-suspend compute clusters.

Column-Level Lineage

Complete visibility from raw data ingestion to executive BI KPIs.

Data Engineering FAQs

Frequently Asked Questions

Guidance on lakehouse migration, streaming architectures, and data governance.

A Lakehouse combines the cost efficiency and multi-format flexibility of object storage (data lakes) with the ACID transactions, schema enforcement, and rapid SQL query performance of traditional data warehouses. It eliminates the need to maintain two separate systems and duplicate petabytes of data.

Data Platform Strategy Call

Design Your Modern Lakehouse & Streaming Pipeline

Connect with our Principal Data Architects to evaluate Databricks/Snowflake migrations, Apache Kafka event ingestion, or dbt analytics modeling.

Modernize Your Data

Build the Modern Data Foundation for Your Enterprise

Connect with our Principal Data Architects to evaluate lakehouse migration, Kafka streaming, or dbt analytics engineering.

Schedule Data Strategy Session Explore Cloud Engineering