SERVICE CATEGORY OVERVIEW

Database & Data Engineering

Legacy database migrations, PostgreSQL DBA, AWS Redshift data warehouses, and Apache Airflow ETL automation.

Service 0140-60% reduction in database licensing & infrastructure costs

Database Migration Services

Overview

End-to-end migration of legacy databases (Oracle, on-prem SQL) to PostgreSQL with zero/minimal downtime.

We provide complete database migration engineeringhandling schema conversion, complex PL/SQL re-engineering, and post-migration performance optimization while preserving complete data integrity and achieving typical 40-60% cost reductions.

Fits and process

Typical fits

Enterprise systems moving from Oracle or on-prem SQL Server to PostgreSQL

Organizations aiming to reduce database licensing and infrastructure costs by 40-60%

Mission-critical platforms requiring zero or near-zero downtime cutover strategies

Complex database architectures with heavy stored procedures and custom trigger logic

How we deliver

01Automated and manual schema analysis, data type mapping, and constraint translation

02PL/SQL to PostgreSQL PL/pgSQL logic re-engineering and automated unit testing

03Parallel CDC / replication pipeline setup for zero or minimal downtime cutover

04Post-migration query performance tuning, index optimization, and connection pooling

05Production failover verification and automated rollback contingency validation

Stack

OraclePostgreSQLPL/SQLPL/pgSQLAWS DMSpgloader

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Service 02Sub-second query response times & 99.99% database uptime

PostgreSQL Database Administration & Optimization

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Overview

Ongoing or specialized DBA engineering to guarantee high availability, performance, and stability.

We manage, optimize, and secure production PostgreSQL clustersfixing replication latency, optimizing slow queries, and architecting fault-tolerant database topologies for mission-critical applications.

Fits and process

Typical fits

High-traffic production applications facing database lock contention or query bottlenecks

Enterprise systems requiring 24/7 uptime, automated backups, and HA streaming replication

Companies seeking expert PostgreSQL DBA support without full-time in-house overhead

Databases needing comprehensive query optimization, index refactoring, and health audits

How we deliver

01In-depth query plan analysis (EXPLAIN ANALYZE) and index structure optimization

02Streaming replication setup, failover testing (Patroni / PgBouncer), and monitoring

03Database parameter tuning (shared_buffers, work_mem, autovacuum parameters)

04Comprehensive security, role-based access, and SSL/TLS encryption audit

05Proactive health checks, lock escalation monitoring, and automated alert rules

Stack

PostgreSQLPgBouncerPatroniPrometheusGrafanaLinux

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Service 03100% data consolidation & sub-second BI query speed

Cloud Data Warehouse Design & Build (AWS)

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Overview

Architecting scalable AWS Redshift data warehouses and modern S3 data lakehouses.

We design and build centralized cloud data warehouses on AWS Redshift from scratch or modernize existing ones, creating robust data marts, fact/dimension table schemas, and unified reporting layers.

Fits and process

Typical fits

Companies consolidating disparate operational data sources into a single source of truth

Organizations scaling past operational DB limits for executive reporting & analytics

Finance & operations teams needing sub-second cross-departmental query results

Businesses building or modernizing an AWS-native cloud data stack

How we deliver

01Redshift cluster architecture, distribution keys, and sort keys design

02Multi-source data ingestion using PySpark, Apache Sqoop, and AWS Glue

03S3-based data lake integration with Redshift Spectrum and AWS Lake Formation

04Dimensional modeling (Star & Snowflake schemas) for business data marts

05Unified reporting view creation for BI tools like QuickSight and Power BI

Stack

AWS RedshiftAWS S3PySparkApache SqoopAWS GlueAWS Lake FormationQuickSight

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Service 0430-50% improvement in pipeline efficiency & turnaround

ETL Pipeline Development & Automation

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Overview

Building robust, automated Apache Airflow data pipelines for reliable data refresh workflows.

We design, orchestrate, and modernize ETL/ELT pipelines to move data smoothly across systems without manual effort, improving pipeline turnaround and processing efficiency by 30-50%.

Fits and process

Typical fits

Organizations struggling with fragile, manual, or failing data refresh scripts

Companies modernizing legacy ETL jobs into cloud-native, DAG-based architectures

Teams requiring automated scheduled data orchestration with real-time alerting

Data engineering teams scaling pipeline throughput and operational efficiency

How we deliver

01Apache Airflow DAG design with modular operators and dynamic scheduling

02Re-engineering legacy SSIS/Informatica/cron scripts into scalable cloud pipelines

03Data validation, schema enforcement, and error handling at every pipeline stage

04Automated retry mechanisms, SLA monitoring, and alerting (Slack/PagerDuty)

05Continuous pipeline optimization for faster data turnaround

Stack

Apache AirflowPythonPySparkAWS GlueSQLDockerGit

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Service 05Zero data-loss migration & concurrent data refresh

Data Lake Architecture & Migration

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Overview

Architecting scalable data lakes and seamless cross-region / cloud data lake migrations.

We build and migrate Hadoop and AWS-based data lakes across regions or hybrid environments with zero data loss, enabling concurrent refresh architecture for faster analytics.

Fits and process

Typical fits

Enterprises managing massive volumes of structured and un-structured data

Organizations migrating on-premises Hadoop clusters to cloud-native S3 data lakes

Multi-region operations needing localized low-latency data access

Businesses seeking 50% throughput improvements via concurrent refresh workflows

How we deliver

01Data lake layout design using open columnar formats (Parquet, ORC)

02On-premise (Hadoop/Hive) to cloud-native (AWS S3/Glue) migration mapping

03Cross-region data replication and bandwidth-optimized transfer pipelines

04Implementing concurrent refresh architecture for continuous dataset updates

05Governance, fine-grained access control, and metadata cataloging via Lake Formation

Stack

HadoopApache HiveAWS S3AWS GlueAWS Lake FormationPySparkEMR

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Service 0625%+ reduction in cloud compute spend

Cross-Cloud Migration (Azure ↔ AWS)

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Overview

Migrating data warehouse environments and pipelines between cloud providers.

We evaluate, plan, and execute seamless migrations between Azure and AWS, rewriting SQL and ETL workflows for target platforms and optimizing compute spend by 25%+ post-migration.

Fits and process

Typical fits

Enterprises standardizing on AWS or migrating away from Azure infrastructure

Organizations seeking to reduce cloud compute and data warehousing costs by 25%+

Companies consolidating multi-cloud complexity into a single efficient platform

Teams needing expert migration of Synapse/Data Factory workloads to AWS Redshift/Airflow

How we deliver

01Comprehensive environment assessment and dependency mapping across clouds

02Target platform schema, SQL script, and ETL logic re-engineering

03Phased data transfer using secure cloud-native migration tools

04Post-migration compute right-sizing, auto-scaling, and query cost optimization

05Validation of data parity, security compliance, and user access roles

Stack

AWS RedshiftAzure Data FactoryApache AirflowPythonSQLTerraform

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Service 07Single source of truth for executive metrics

Analytics & Business Intelligence

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Overview

Transform scattered enterprise data into live interactive BI dashboards.

We build dashboards and reporting layers directly connected to your operational data warehouse enabling automated, self-serve analytics for decision makers.

Fits and process

Typical fits

Executive leadership teams needing a unified reporting source of truth

Operations managers tracking real-time KPI metrics and SLA targets

Finance and growth teams building automated financial models and forecasts

How we deliver

01Define key performance indicators (KPIs) with business stakeholders

02Connect, clean, and structure underlying warehouse tables for BI tools

03Build intuitive dashboards with interactive drill-down capabilities

04Configure automated email/Slack report delivery schedules

05Train internal teams for self-serve ad-hoc data exploration

Stack

Power BIMetabaseSupersetSQLPython

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