Data Engineering

Big Data Solutions

Pipelines, platforms and analytics your business can trust

SyncScrum builds the data engineering behind reporting, analytics and AI: pipelines from every source, a modelled warehouse or lake, real-time streams where they matter, dashboards on top, and the quality checks that keep the numbers honest.

Book a data audit What we build

Sources

Apps · APIs · files

Ingest

Kafka · batch

Store

Warehouse · lake

Model

Transform · quality

Serve

Dashboards · APIs · AI

Freshness

Quality checks

Schema contract
Reconciliation
Volume alert

Kafka, PostgreSQL, Elasticsearch, Redis, Python
Batch and real-time in one platform
Quality and lineage designed in

The six parts of a working data platform

Most data problems are not storage problems. They are trust, freshness and access problems. Each part below addresses one of them.

Data pipelines

Batch and incremental ingestion from applications, APIs, files and third-party systems, with validation at the door.

Warehouses and lakes

A modelled, queryable store on managed cloud services, sized for the questions you need answered.

Real-time streaming

Kafka-based event streams and processing for dashboards, alerts and features that cannot wait for a nightly job.

Transformation and modelling

Clean, documented data models that analysts and applications can use without re-deriving business logic.

Dashboards and data APIs

Custom dashboards in your product or internal tools, and APIs that serve data to other systems.

Data quality and governance

Contracts, monitoring, lineage and access controls so the numbers can be trusted.

Symptoms

Signs the data platform needs engineering

You do not need petabytes to need this. You need any of the following.

Reports disagree
Two dashboards, two numbers, because business logic is re-implemented in every query.
Reporting slows the product
Analytics queries run against the production database and everyone feels it.
Data arrives late or silently wrong
Nobody knows a feed broke until a customer or an executive notices.
Sources cannot be joined
CRM, billing, product and support data live apart, so the obvious questions cannot be asked.
Real-time needs a nightly job
Alerts, fraud checks or live dashboards that wait until tomorrow.
AI needs clean input
An AI initiative stalled because the data it needs is not modelled or trustworthy.

Batch and real-time, one platform

Two paths, one modelled store, so history and the present agree.

Batch path

Scheduled and incremental loads from databases, files and APIs. Validated at ingestion, transformed into documented models, reconciled against sources.

Streaming path

Events published to Kafka from applications and services, processed as they arrive, serving live dashboards, alerts and features with second-level freshness.

Serving layer

Modelled tables for BI tools, Elasticsearch for search and log analytics, Redis for hot reads, and data APIs for other systems and AI features.

Quality and operations

Schema contracts, freshness and volume monitoring, lineage, access control and alerting, so failures are seen by engineers before they are seen by the business.

How it runs

From data audit to a platform in use

Step 1: Data audit
Sources, volumes, quality, the questions the business needs answered and the cost of answering them today.
Findings and target architecture
Step 2: First pipeline
The pipeline and model that unblock the most valuable report, delivered end to end, so value arrives early.
First trusted dataset
Step 3: Platform build-out
Remaining sources, streaming where needed, modelling, serving layer and quality monitoring.
Working platform
Step 4: Dashboards and access
Custom dashboards, BI connections and data APIs, with access controls.
Data in daily use
Step 5: Operate and extend
Monitoring, new sources, cost tuning and the data foundations for AI features.
Reliable, growing platform

Building AI on top? See AI development.

Data technology we work with

Streaming and queues

Apache Kafka
RabbitMQ
Redis

Storage and search

PostgreSQL
MySQL
MariaDB
MongoDB
Elasticsearch

Processing

Python
Node.js
Go
Spring Boot

Cloud

AWS
Google Cloud
Azure
Docker
Kubernetes
Terraform

Big data questions, answered

What do you mean by big data solutions?

The engineering that turns scattered, growing data into something a business can query, trust and act on: ingestion pipelines, a warehouse or lake, transformation and modelling, real-time streams where needed, dashboards and APIs on top, and the quality and governance that keep it reliable.

Do we actually have big data?

Volume is only one part. Many companies have moderate volume but many sources, fast-changing data, or reporting needs their current database cannot serve. If your data cannot be joined, trusted or queried fast enough, this service applies whether it is gigabytes or terabytes.

Which technologies do you use?

PostgreSQL, MySQL and MongoDB as sources and serving stores, Apache Kafka and RabbitMQ for streaming and queues, Redis for caching, Elasticsearch for search and log analytics, Python and Node.js for pipelines, and managed warehouse and storage services on AWS, Google Cloud or Azure.

Can you build real-time analytics?

Yes. Event streams through Kafka, stream processing, and serving layers that keep dashboards and alerts current within seconds, alongside the batch pipelines that handle history and reporting.

How do you make sure the data is correct?

Schema contracts at ingestion, validation and reconciliation steps in the pipeline, freshness and volume monitoring, lineage documentation and alerts when something drifts. Bad data that arrives quietly is the main risk in any data platform, so this is designed in.

Do you build dashboards and reports?

Yes: custom dashboards in your product or internal tools, and modelled data ready for BI tools your team already uses.

Can this feed AI and machine learning work?

Yes. Clean, modelled, well-governed data is the prerequisite for reliable AI features. Our AI development service builds on the platforms this service creates.

How do you start?

With a data audit: sources, volumes, quality, the questions the business needs answered and what it costs to answer them today. That produces a target architecture and a phased plan, usually starting with the pipeline that unblocks the most valuable report.

Data ready for AI?

AI Development

AI features and retrieval systems built on clean, modelled data.

View service

Moving to the cloud?

Cloud Migration

Moving data platforms and databases to managed cloud services.

View service

Need the tools on top?

Custom Software Development

The dashboards, portals and internal tools that sit on top of the data.

View service