Traditional Data System: Complex, Costly and Slow
In most organisation, data moves through a rigid, multi-step process before it's ready to use. This approach requires data engineering team to manage multiple software, transform, move data from one system to another and manage it separately to retrieve and process data when needed. The engineering team needs to optimize performance and scalability and also make sure that system doesn’t break.
Traditional data system
1. Data Collection - Connect multiple sources through data ingestion software tools
2. Temporary Storage - Temporarily store data for next step
3. Data Transformation - Standardise formats, ensure quality and prepare data to make sure good quality data enters pipeline
4. Batch jobs - Send data to warehouse at fixed interval for retrieval and analysis
5. Distribution - Deliver processed data to various systems for report, streaming or long term analysis
Even when everything works perfectly, this process can take anywhere from 1 hour to an entire day. This delay makes real-time decision making nearly impossible.
As data sources and types increase gradually, building and maintaining data pipelines becomes even more challenging. Engineering teams are forced to write complex, case-specific code with little reusability. Every new use case means starting from scratch. Data quality checks become harder and bad data often slips through undetected.
The result?
High costs, slow performance, and missed opportunities.
The Datablit Way: Transforming Customer Experience with Unified, Instant, Scalable, & Seamless Data Flow
Frustated by rising costs and slow performance of traditional data system, Datablit takes a completely different approach — replacing complex, multi-system data movement with a unified customer data platform that works in real time. With Datablit, millions of datapoints are connected, processed, and ready for action without the need to manually orchestrate storage to application flows. This means no bottlenecks, no hours-long wait and no costly re-engineering for every new business use case scenario.
How Datablit Works
1. Automatic Ingestion & Data Standardisation - Collects data from multiple sources and formats it instantly
2. Continuous Real-Time Transfer - Sends data directly to the warehouse in batches every second
3. Immediate Availability - Data is ready for analysis, personalisation or automation the moment it arrives
With Datablit as the central platform, data engineer, developer and business team can self serve — accessing and acting on data the instant it’s created. The result is faster strategy execution, scalable personalisation and more reliable insights at a fraction of the time and cost.
Hidden Cost of Relying on Fragmented Data for Business Execution
In most companies, turning a new business strategy into reality is a long, code heavy process. Product and marketing team relies heavily on the data engineering team to write extensive code, manage large scale data-flows and implement quality checks. As data volumes surge, systems often struggle, pipelines fail, errors go unnoticed, and identifying root causes becomes a time-consuming computational and operational challenge.
The result? Strategies take months to launch. By the time they go live, the opportunity has often passed.
With Datablit, businesses can create business strategy via rules and signals within minutes — no code, no engineering bottlenecks. Easy to create rules, and signals which can be tracked in batch (at specific intervals) and realtime(streaming mode), Datablit handle the complexity of scaling, data management and quality assurance.
1. Upto 90% Time & Cost Savings
2. Deep Monitoring - Full visibility into rules, signals and performance/ROI of business strategies
3. Instant Adaptability - Create new rule and signals, make changes on the fly and keep strategy fresh with changing customer need
Datablit turns weeks of engineering effort into minutes of business action.
Quick Reference: Key Terms
Data Source
The internal and external systems that collect business and customer data. Most business generate data from multiple systems and software.
Data Ingestion
Gather data from multiple sources — the internal and external systems that collect business and customer data. Most businesses gather data from multiple systems and software.
Data Transformation
It is process of taking raw data that has been extracted from data source and turning into usable datasets — changing messy information into clean, quality, trusted data that business can use to meet operational needs and actionable insights. It is key process in data engineering.
Data Pipeline
A data pipeline is the way data flows from one system to another. It consists of a series of steps that are carried out in specific order, with the output of one step acting as the input for the next step. There are usually three key elements: the source, data processing steps and finally the destination.
Data Warehouse
A data warehouse is a centralized repository that consolidates current and historical data from various sources, enabling comprehensive analysis. Its architecture is meticulously crafted to optimise data storage, enable high performance and support scalable analytical workloads.