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Stream processing Data governance ML platform Analytical data platform Cloud-native data platform Big data DevOps

Companies that successfully compete, innovate and win in modern business environments are data-driven. While enterprises have access to huge amounts of internal and external data, converting these raw figures to actionable insights is a complex process. All the raw data must be gathered in one place, then processed, cleaned, connected and structured. Next, it has to be made accessible to business people via reports, dashboards and analytical tools. Finally, machine learning and artificial intelligence are applied to learn underlying patterns and identify the next best actions. 

We started our work in this field by helping clients build scalable data platforms based on high-performance computing technology. When the first versions of MapReduce and Hadoop became available, we saw great value in merging the concept of high-performance computing with big data. Since then, we have helped a number of large and small clients in the technology, media, retail and financial services industries build analytical data platforms based on open source technology stacks, both on premise and in the cloud.

Helping customers turn data into insights since 2008

Foundational platform

A solid base platform is the key to implementing modern business intelligence successfully. Building a base platform requires careful design of storage and compute fabrics, as well as the implementation of security, data governance, CI/CD processes and quality engineering. Nowadays, enterprises have two major choices to make:

  • Build the platform in the cloud. Google, Amazon and Microsoft provide strong cloud-native options for building data lakes, data warehouses, analytics and machine learning capabilities. Sticking with the cloud helps reduce initial implementation costs and cuts the time to market.
  • Leave data in private data centers. Any modern Hadoop distribution can serve as the foundation for a data platform. Open source technologies, like Apache Spark, Beam, Hive, NiFi, Kubeflow, ElasticSearch and many others provide a good ecosystem to cover all enterprise needs with an open data stack.

Data processing pipeline

Getting the first influx of data into the platform breathes life into it. However, raw data is rarely useful. To increase its value, data should be cleaned, processed and structured, and often data from different sources need to be joined together.

There are two major techniques to process data: batch and in-stream. In the world of microservices, importing data from systems-of-record into an analytical data platform is a challenge for many companies. Constructing modern architectural patterns, such as event sourcing and CQRS, helps with this difficulty. When integrations with legacy systems are needed, a number of open source technologies assist with implementing traditional batch importing and processing.

Last but not least, building data lineage and data quality capabilities ensures that the data stays up-to-date.

Analytics and machine learning

Once the data is in the platform, it needs to be utilized. There is a number of ways to turn data into actionable insights:

  • Provide data analysts with convenient access to the data in the platform for manual analysis.
  • Generate reports and dashboards with various metrics and KPIs, which the business may use.
  • Build a machine learning platform, so that data scientists can implement modern artificial intelligence and machine learning algorithms.
  • Implement a decision portal, where business users can utilize AI and ML algorithms built by data scientists to get actionable insights automatically.
Scalable data lake

Scalable data lake

Gather and store both raw and processed data in a data lake that can scale to meet demand.
Data processing pipeline

Data processing pipeline

Import and process data of any complexity from any data source, in either stream or batch mode.
Data warehouse

Data warehouse

Provide quick and easy access to structured data after it has been processed.
Reporting and dashboarding

Reporting and dashboarding

Generate reports and provide dashboards to business users for analysis of KPI's.
Data access layer

Data access layer

Provide data analysis and convenient access to data for manual analysis for advanced users.
Data science and machine learning platform

Data science and machine learning platform

Increase productivity of data scientists and ML engineers at all stages, from data preparation to sharing of insights.
Data governance

Data governance

Keep data under control with metadata management and data lineage.
Data quality

Data quality

Ensure data correctness, and prevent corruption from spreading in the lake.
Business decisions portal

Business decisions portal

Allow the business to operate on high-level goals by providing self-service to AI/ML algorithms.
Cloud or on-premise deployment

Cloud or on-premise deployment

Minimize implementation time, reduce time-to-market and save on maintenance costs.
Security

Security

Protect access to sensitive data with access control, encryption and tokenization.
Continuous delivery

Continuous delivery

Automate delivery and testing process of data engineering jobs and pipelines.
How it works
Cloud platform service by Google
Online file storage service
Fully-managed cloud service for running clusters
Stream and batch processing
Real-time messaging service
Compressed data storage system
Interactive analysis of large datasets
Tool to analyze and visualize data, and build machine learning models
Cleans and prepares structured and unstructured data for analysis
Data workflow orchestration service
Trains machine learning models
Big data processing and analysis
Object storage service
Secure computing capacity in the cloud
Cloud data warehouse
Collects, processes, and analyzes streaming data in real-time
ETL service
Serverless, interactive query service
Machine learning platform
Data processing, storage and computation platform
Cluster-computing framework
Programming model to assist with data processing pipelines
Data warehouse for query and analysis
Automates flow of data between systems
Workflow management system
Workflow scheduling system
Stream-processing platform
Transfers data between Hadoop and relational databases
Open source search engine
Data visualization and monitoring
Data governance and metadata management
Cloud platform service by Google
Online file storage service
Fully-managed cloud service for running clusters
Stream and batch processing
Real-time messaging service
Compressed data storage system
Interactive analysis of large datasets
Tool to analyze and visualize data, and build machine learning models
Cleans and prepares structured and unstructured data for analysis
Data workflow orchestration service
Trains machine learning models
Big data processing and analysis
Object storage service
Secure computing capacity in the cloud
Cloud data warehouse
Collects, processes, and analyzes streaming data in real-time
ETL service
Serverless, interactive query service
Machine learning platform
Data processing, storage and computation platform
Cluster-computing framework
Programming model to assist with data processing pipelines
Data warehouse for query and analysis
Automates flow of data between systems
Workflow management system
Workflow scheduling system
Stream-processing platform
Transfers data between Hadoop and relational databases
Open source search engine
Data visualization and monitoring
Data governance and metadata management

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