Data Engineering Solutions

"The world's most valuable resource is no longer oil, but data"

Data Platform

We build Data Platforms that allow you to run your ETL/ELT jobs in a scalable fashion. The platform also automates training and deploying Machine Learning models for you. We can help modernizing your outdated ETL tools with Cloud agnostic Data Engineering tools.

A Data Lake is a central hub for storing and analyzing any structured and unstructured data in any scale. We can set up your Data Lake, ingest data from your operational databases, data warehouses or any data sources in both batch or real-time, build models on top of it and make the data accessible to all your organization.

Clickstream Analytics

Gather information from your website, mobile application or IoT device, track every click and user behaviour. Our Clickstream Analytics solution provides you to collect, store and analyze your massive data in an easy way. You can use the data to improve your service quality or customer satisfaction.


Solution #1: Data Platform

a Data Platform is an infrastructure that enables organization to build scalable data pipelines.

Integration

Our Data Platform solution has built-in integration with the most common data sources and it’s easy to integrate custom data sources as

Flexibility

The platform infrastructure is flexible, can be built on any cloud or on-premise infrastructures. The workloads can run distributed on relatively smaller instances, so they run much more faster and don’t rely on expensive hardware.

Scalability

Cost Effectiveness

Our platform supports various containerization management systems such as ECS, Kubernetes, etc. to scale and run its workloads.

The platform is built with infrastructure-as-code principles. The workloads can be integrated to CI/CD pipelines.

Automation


Solution #1: Data Platform

Batch Data Processing

Batch data processing is efficient and cost effective for most use-cases. The platform can process billions of rows of data every day.


Solution #1: Data Platform

Real-time Data Processing

Real-time data processing is crucial for some specific use-cases like Finance.


Solution #1: Data Platform

Built-in Supported Data Sources

RDBMS NoSQL DWH/DL Web/FTP
Microsoft SQL Server mongoDB Amazon Redshift REST API
PostgreSQL Apache HBASE Amazon S3 FTP
MySQL Amazon DocumentDB hadoop HDFS
ORACLE DATABASE Apache Solr elastic

Solution #2: Data Lake

A Data Lake is a central hub for storing and analyzing any structured and unstructured data in any scale

Key advantages of a Data Lake

  • Data Modeling: Store and analyze TB/PB of data, build models on top of it without need to chance the actual data.
  • Scalability: Can scale both storage and processing separately based on demand.
  • Availability: Cloud technologies allow you to start building a Data Lake within minutes. Failed resources will be replaced automatically.
  • Maintainability: Anything can be automated using infrastructure as code technologies.
  • Low Cost: No need to pay for licences, no need for expensive storage hardware, etc.

Solution #2: Data Lake

Data Lake Architecture

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Solution #2: Data Lake

Data Warehouse vs Data Lake

Data Warehouse Data Lake
Data Relational from transactional systems, operational databases, and line of business applications Non-relational and relational from IoT devices, web sites, mobile apps, social media, and corporate applications
Schema Designed prior to the DW implementation (schema-on-write) Written at the time of analysis (schema-on-read)
Price/Performance Fastest query results using higher cost storage Query results getting faster using low-cost storage
Data Quality Highly curated data that serves as the central version of the truth Any data that may or may not be curated(ie. raw data)
Users Business analysts Data scientists, Data developers, and Business analysts(using curated data)

Solution #3: Clickstream Analytics

Clickstream Analytics allows organization to get more insight about the customers and enables new opportunities.

Clickstream Analytics allows you to get to know your customers better. Analyze, measure and improve your products, engage better.

Know your customer

Understand the trends in near real-time, build strategies.

See the new opportunities

Customize your campaigns, target user segments or even personalize them, improve conversion.

Build campaigns


Tech Stack

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References