Data Engineering

Build scalable, reliable, and high-performance data ecosystems that help organizations unlock insights, improve decision-making, and accelerate digital transformation initiatives.

We help businesses design, modernize, and manage data platforms capable of handling structured, unstructured, and real-time data across multiple systems and cloud environments.

Certifications
AppleTech Data Engineering
— Data Infrastructure
End-to-End Data Pipeline
DATA SOURCES DATABASE REST API GET /data 200 OK · JSON STREAMS Kafka · Kinesis INGESTION INGEST BATCH STREAM CDC Airflow · Fivetran Debezium · NiFi RAW STORAGE DATA LAKE Parquet · Avro ORC · Delta S3 · ADLS · GCS TRANSFORM ETL ELT CLEAN ENRICH VALIDATE dbt · Spark Glue · Dataflow WAREHOUSE DATA WAREHOUSE FACT DIMENSION AGGREGATE / MART Snowflake · BigQuery Redshift · Synapse BI · ML · Analytics 01 · SOURCE 02 · INGEST 03 · STORE 04 · TRANSFORM 05 · SERVE
— Query Language
SQL & Database Schema Design
query.sql — SQL Editor 01 02 03 04 05 06 07 08 09 10 11 12 13 SELECT o.order_id, c.customer_name, SUM(oi.amount) AS total FROM orders o JOIN customers c ON o.cust_id = c.id JOIN order_items oi ON oi.order_id = o.id WHERE o.status = 'COMPLETED' GROUP BY o.order_id, c.customer_name ▶ 1,248 rows · 0.032s · Execution: OPTIMIZED SCHEMA · ERD ORDERS 🔑 order_id INT PK cust_id INT FK status VARCHAR created_at DATE total DECIMAL CUSTOMERS 🔑 id INT PK customer_name email VARCHAR segment TEXT ORDER_ITEMS 🔑 item_id INT PK order_id INT FK product_id INT amount DECIMAL 1 : N 1 : N
— Data Transformation
ETL / ELT Transform Engine
ETL Extract → Transform → Load EXTRACT SOURCE A SOURCE B SOURCE C Full Load Incremental CDC Fivetran Airbyte TRANSFORM FILTER DEDUPE NORMALIZE JOIN AGGREGATE CAST TYPES Spark · dbt Glue · Beam LOAD DW TARGET Snowflake BigQuery ELT Extract → Load → Transform EXTRACT Raw Data Unstructured Semi-struct. Airbyte LOAD Raw Zone Snowflake TRANSFORM dbt Models SQL Views Stored Procs Materialise staging/ intermediate/ marts/ dbt Core dbt Cloud ELT leverages cloud DW compute power for in-place transformation
— Analytics Layer
Data Warehouse & Dimensional Modelling
STAR SCHEMA FACT SALES order_key FK product_key FK amount MEASURE DIM DATE date_key PK year · month · day DIM CUSTOMER customer_key PK name · segment region · tier DIM PRODUCT product_key PK name · category brand · price DIM LOCATION location_key PK city · country DW LAYERS PRESENTATION / BI LAYER DATA MARTS INTEGRATED DW LAYER STAGING / ODS LAYER RAW / LANDING ZONE PLATFORMS: Snowflake BigQuery Redshift Synapse
— Streaming & Orchestration
Real-Time Pipelines & Workflow Orchestration
REAL-TIME STREAMING PRODUCE App Events IoT Sensors Clickstream Logs · CDC KAFKA topics · partitions PROCESS Filter Aggregate Join Flink · Spark Str. SINK S3 DB ES Redis THROUGHPUT · msgs/sec 48K/s LATENCY · p99 ms 12ms Apache Kafka · Confluent · Amazon Kinesis Apache Flink · Spark Structured Streaming Google Pub/Sub · Azure Event Hubs ORCHESTRATION · DAG START extract_data validate_src transform_dbt load_to_dw notify_done Airflow · Prefect · Dagster · Mage
— Quality & Governance
Data Quality, Cataloguing & Governance
DATA QUALITY SCORECARD COMPLETENESS 98% ACCURACY 95% CONSISTENCY 91% TIMELINESS 99% QUALITY CHECKS CHECK TABLE STATUS not_null(order_id) orders PASS unique(customer_id) customers PASS accepted_values(status) orders WARN row_count > 0 order_items PASS Great Expectations · dbt Tests · Soda Core · Monte Carlo DATA LINEAGE raw_orders Source · S3 stg_orders Staging · dbt int_order_items Intermediate dim_customers Dimension · DW fct_sales Fact · DW DATA CATALOG Apache Atlas · DataHub · Collibra Alation · Amundsen · OpenMetadata
INITIALIZING DATA PIPELINE...

Scalable Data Engineering Services for Analytics, Automation, and Enterprise Transformation

Our data engineering services enable organizations to collect, process, store, and analyze large volumes of data efficiently. From data pipelines and warehousing to analytics and AI-ready architectures, we create robust foundations for data-driven businesses.

Whether you are modernizing legacy systems or building a cloud-native data platform, our engineers help streamline your entire data lifecycle.

DevOps Consulting Services

Scalable Data Pipelines

Develop reliable ETL and ELT pipelines that automate data collection, transformation, and synchronization across enterprise applications, APIs, cloud systems, and databases for faster and accurate business insights.

DevOps Managed Services

Real-Time Data Processing

Enable live analytics and event-driven architectures with real-time data streaming solutions that support operational intelligence, monitoring, automation, and faster decision-making capabilities.

Application and Cloud Management

Data Warehousing Solutions

Create centralized and optimized data warehouses that improve reporting accuracy, business intelligence, analytics performance, and enterprise-wide access to structured organizational data.

CI-CD Pipeline

AI-Ready Data Architecture

Prepare clean, governed, and scalable data foundations that support machine learning, predictive analytics, LLM applications, and intelligent automation initiatives across business operations.

We help building reliable, scalable, and intelligent data ecosystems designed to support automation, analytics, and AI-driven business operations.

GET YOUR SOLUTION

Why Choose Us For Data Engineering?

We work with enterprise-grade databases including SQL Server, MySQL, PostgreSQL, MongoDB, Oracle, and Redis to build scalable, secure, and high-performance data infrastructures for modern business applications.

Our engineers leverage technologies such as Python, Apache Spark, Kafka, Airflow, Hadoop, and dbt to develop robust ETL pipelines, real-time processing systems, and large-scale data transformation workflows.

We build cloud-native data ecosystems using Snowflake, Azure Data Factory, Azure Synapse, Databricks, AWS Redshift, and BigQuery to enable scalable analytics, centralized reporting, and intelligent data operations.

We utilize modern analytics and visualization platforms including Power BI, Tableau, Looker, Elasticsearch, and Kibana to help organizations gain actionable insights through interactive dashboards and reporting solutions.

React
PyTorch
Python
PHP
Microsoft NET
JAX
Jasper AI
Gemini
flutter
Flow
Core ML
Claude AI
ChatGPT
Apple
Angular
Android
AWS
Azure
MongoDB
MSSQL Server
MySQL
PostgreSQL
Vue
Weaviate

Our Case Study

Turning challenges into measurable outcomes.

eWallet Solution

eWallet Solution

Bank Locator Mobile App

Bank Locator Mobile App

Learning Management System

Learning Management System

Sales & Inventory Management

Sales & Inventory Management

Medical Practice Management

Transforming Patient Care with Medical Practice Application

Property Listing and Management

Property Listing and Management

Sports Talent Discovery Platform

Sports Talent Discovery Platform

CDR Data Management

CDR Data Management

#$

FAQs: DevOps

What are data engineering services?

Data engineering services involve designing, building, and managing systems that collect, process, transform, and store data for analytics, reporting, and AI applications.

Which technologies do you work with?

We work with SQL Server, Python, Snowflake, Databricks, Azure Data Factory, Apache Spark, Kafka, Power BI, PostgreSQL, MySQL, and multiple cloud platforms.

Can you modernize legacy data systems?

Yes. We help organizations migrate and modernize legacy databases, reporting systems, and data infrastructure into scalable cloud-native platforms.

Do you support real-time data processing?

Yes. We develop real-time streaming and event-driven architectures for live analytics, monitoring, and operational intelligence solutions.

Can your data engineering solutions support AI initiatives?

Absolutely. We create AI-ready data architectures optimized for machine learning, predictive analytics, LLM applications, and intelligent automation systems.

Do you provide dedicated data engineering resources?

Yes. We offer dedicated data engineers, ETL developers, SQL developers, BI specialists, cloud data engineers, and complete data engineering teams based on project requirements.