CyberVision offers full-stack data analytics solutions and services, backed by our portfolio of successful projects that encompass each part of the Big Data lifecycle — from data collection to warehousing, data analysis, BI and reporting. We have in-depth expertise in modern open-source and commercial tools for Big Data, machine learning, and artificial intelligence. We also know how to apply those tools to resolve real-life problems and help companies succeed with their digital strategies.
Enterprise analytics based on predictive modeling to address more accurate operational and strategic planning, detect early signs of equipment malfunctions, and ensure better tracking of business KPIs
Cloud-based analytics portals
Advanced cluster management and data science applications for Big Data professionals and companies offering commercial Big Data solutions
Business intelligence & reporting solutions
Modern business intelligence solutions powered by the latest technologies and capabilities that include predictive modeling automation, real-time and historical analytics, ML applications, etc.
Automated data analytics pipelines
Automated ETL / ELT data analytics pipelines with end-to-end system integration, from a data source to a data warehouse
ML/AI models and algorithms
Development and implementation of machine learning and artificial intelligence models, performance review and optimization, and ML/AI-based application development
End-to-end IoT analytics
Edge to cloud IoT data analytics, including real-time and historical device data analytics, on-device analytics, smart alerts, predictive maintenance, A/B testing, etc.
Specializing in all types of Big Data solutions, we’ve gathered an outstanding team of experts who can get your project moving in the right direction from day one. We can assist you in clarifying requirements and polishing your go-to-market strategy as well as in building the solution that fulfills your business objectives.
ML / AI modeling
Implementation and custom development
Commercial technology development
Vendor and technology migration
We have vast experience using popular open-source data analytics tools under the Apache foundation. We’ve also actively contributed to the Apache Hadoop ecosystem development with our own add-ons and improvements.
Our number one choice is Apache Spark, which is a powerful data processing and machine learning engine for large-scale distributed Big Data applications. It is also widely utilized under the hood of popular commercial analytics solutions.
We use Apache Drill to deliver fast and secure self-service BI SQL analytics at scale. Our team has contributed to the Drill codebase and implemented solutions on top of it.
We often use Hadoop in conjunction with Apache Spark and NoSQL databases to provide data storage and management for Spark-powered data pipelines. Modern implementations of Hadoop also rely on an ecosystem of related solutions, such as Hive, Impala, HBase, and Zeppelin that offer a rich set of Big Data analytics functionality.
Another popular technology that we widely use in our projects is Apache Kafka, an event streaming platform that combines messages, storage, and data processing, and can be also used for real-time analytics.
For companies using Amazon’s cloud infrastructure, CyberVision offers in-depth expertise in Amazon Web Services data analytics and machine learning solutions.
Amazon Kinesis makes it easy to capture, transform, and analyze streaming data for near real-time analytics. The service enables you to capture real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications.
Amazon Elasticsearch Service is a fully managed service that enables developers to easily deploy and run the popular Elasticsearch framework. The service provides support for Elasticsearch APIs, managed Kibana, integration with Logstash and other AWS services, and built-in alerting and SQL querying.
AWS Deep Learning AMIs provides ML professionals with the infrastructure and tools to set up deep learning in the cloud. It supports all popular deep learning frameworks and allows developers to effectively train their ML models at any scale.
Amazon QuickSight is a fully managed BI service that makes it easy to create, share, and manage interactive dashboards that include ML insights.
Amazon SageMaker is a fully managed service that speeds up building and training machine learning models by offering ML developers a range of automation and monitoring tools.
As an official Google Cloud solution integration partner, CyberVision is proficient in implementation, custom development, and performance tuning of the Google Cloud Big Data / ML / AI stack of cloud services.
GCP BigQuery provides data storage and analysis services in Google Cloud. It is fast, scalable, and cost effective. It is one of our favorite tools, which we use in all kinds of analytics solutions that we build on GCP.
GCP Dataflow provides a managed service and set of SDKs that we use to perform batch and streaming data processing tasks.
GCP AI Platform combines the managed infrastructure of GCP with the power and flexibility of TensorFlow. It can be effectively used to train machine learning models at scale, and to host trained models to make predictions about new data in the cloud. This is a go-to solution for our ML / AI projects.
GCP Cloud Data Fusion (CDF) is a fully managed, cloud-native data integration service that helps users efficiently build and manage ETL / ELT data pipelines. CyberVision was among the first companies to successfully utilize this innovative solution for a large-scale enterprise project.