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DataOps

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 jobs in a scalable fashion. The platform also automates training and deploying Machine Learning models for you. We can help your transition from outdated ETL tools to Open-source Big Data tools.


Data Lake
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 batches or real-time, build models on top of it and make the data accessible to all your organization.
Big Data Analytics
Gather information from your website, mobile application or IoT device, track every click and user behaviour. Our Big Data 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.


Data Platform


Data Platform is an infrastructure that enables an 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 well.

Flexibility      

The platform infrastructure is flexible, can be built on any cloud or on-premise infrastructures.

Scalability     

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

Cost
Effectiveness

The workloads can run distributed on relatively smaller instances, so they run much more faster and don’t rely on expensive hardware.

Automation   

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

Batch Data Processing

DataOps Data Platform

 

Batch data processing is efficient and cost-effective for most use-cases.

The platform can process billions of rows of data every day.

 

 

 

Real-Time Data Processing

Real Time Data Processing

 

 

 

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

 

 

 

 

 

Built-in Supported Data Sources

RDBMS

NoSQL

DWH/DL

Web/FTP

                    

                  

               

                 


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.


Data Lake ArchitectureData Lake Architecture


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)

 

Big Data Analytics


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


-Know your customer
Big Data Analytics allows you to get to know your customers better. Analyze, measure and improve your products, engage better.

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

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

Our solution for a data size of more than 100 TB. with Standard SQL experience: Sqream


Energize Your Massive Data Analytics

Due to exponentially growing data, organizations today are facing slowdowns, with analytics taking hours or even days. Time-consuming preparation is needed for each change in perspective, and some complex analytics simply cannot be done.

20 x MORE DATA | 100 x FASTER | 10 % OF COST

SQream’s GPU data warehouse enables rapid analysis of terabytes to petabytes of raw data, eliminating the need for arduous preparation while reducing reporting time from hours to minutes.
SQream complements your MPP or Hadoop-based system with a simple "lift and shift" of raw data to SQream DB, allowing you to focus on insights instead of infrastructure.

 

 

 

Data Exploration Made Easy

Fast Analysis of Massive Raw Data: SQream’s powerful technology breezes through trillions of rows of data, getting you results up to 100x faster. With SQream, your raw data is available for immediate querying, so there’s no need for pre-aggregation or pre-modelling. 

Built for Your Growing Data: Grow from terabytes to petabytes with ease. SQream easily scales storage and compute power, with no need for data redistribution.

Simple Deployment & Administration: With standard SQL syntax as well as ODBC, JDBC, .NET, Node.js and Python connectivity, SQream DB is already supported by your ecosystem - either on the cloud or on-premise.

Cost-Efficient: Harnessing the tremendous power of NVIDIA GPUs, SQream offers a minimal footprint with maximum hardware efficiency. SQream can store and analyze over 100 TB in a single 2U machine.


SQream Architecture


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