Spark
It is a distributed computing engine designed for fast in-memory data processing, enabling both real-time and batch data analysis.
Spark-based solutions allow for efficient processing, analysis, and integration of data from multiple sources — both in the cloud and on-premises.
With support for multiple programming languages (Scala, Python, Java) and integration with popular Big Data tools, Spark serves as the foundation for modern data-driven projects.
Spark solutions include:
Batch and Stream Processing
Supports large-scale data processing in both batch and real-time modes, ideal for analytics, ETL pipelines, and event processing.
In-Memory Computing
- Data is processed in RAM, reducing disk I/O and drastically improving performance, suitable for iterative machine learning and large-scale computations.
Data Integration and ETL
- Integrates easily with Hadoop, HDFS, Hive, Kafka, and a wide range of databases for efficient transformation, cleaning, and aggregation of data.
Machine Learning and AI Support
- Built-in MLlib library enables scalable machine learning algorithms for clustering, classification, regression, and recommendation systems.
Graph and SQL Analytics
- Advanced data analysis using Spark SQL and GraphX for querying and graph processing, enabling BI reporting, fraud detection, and network analysis.
Multi-language API
- Supports Scala, Python, Java, and R, allowing teams to work in their preferred environment while collaborating on one platform.
Spark: Lightning-fast data processing, real-time analytics and enterprise scalability
High performance
In-memory computation speeds up processing significantly.
Horizontal scalability
Runs seamlessly from single machines to large clusters.
Versatile use cases
Powers ETL, real-time analytics, machine learning, and more.
Strong Big Data ecosystem integration
Compatible with Hadoop, Kafka, Cassandra, etc.
Vibrant community and active development
Continuous updates and innovations.
Unified data platform
One solution for batch, streaming, AI, and SQL.
Spark consulting in the project
Fast analysis of large datasets
Speeds up operational decisions.
Real-time GPS data processing
Spark Streaming enables real-time processing of GPS data.
Scalability
Ensures system performance under increasing load.
Spark in practice | Case Studies
CBIF
About the project
Trans.eu is one of the biggest freight exchanges in Europe. Every day the exchange connects thousands of carriers, shippers and forwarders to transport cargoes all over the continent. Before entering into transactions customers of the exchange needed information about their contractors. We helped them create a system that displays detailed data prior to entering into a transaction in the system. This is how the CBIF was created.
