DataStork Technology Blog

Curation Service Build Path

Curation Project Summary A leading financial publisher needed a way to measure the performance of an AI-driven semantic document classification and information extraction pipeline from large-volume news feed. The service was developed using agile methodology over a period of one

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Spring Transactional Proxies

Here we will discuss the Spring wonderland of proxies and database transactions. Often we need to create Spring services that work with database transactions. First we define an interface, for example let’s take a simple UsersService like this: public interface

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Data Analytics with Zero Latency and High Precision?

Everyone is “doing analytics” these days Data Analytics is an IT buzzword. Hundreds of paradigms and solutions: change-data-capture, ETL, ELT, ingestion, staging, OLAP, data streaming, map/reduce, stream processing, data mining,… Amazon Redshift and Lambda; Apache Kafka, Storm, Spark and Hadoop

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Services Summary

DataStork offers state-of-art software engineering for organizations of various sizes and domains. We create a craft we take pride in and form partnerships we care about. See DataStork’s market offering: DataStork-Services

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Technical Matrix

Experienced engineers are those that do not head-jump on the solution with hipster next-great-thing technology approach, rather carefully evaluate the unique customer needs and apply the right technology combination for optimal results. Like an experienced craftsman, each DataStork engineer masters

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Case Study: Rapid Development in the AI Domain

Rapid development in complex domain? DataStork applied combination of AI experience and state-of-art goal-oriented software development process to successfully deliver comprehensive application in just 2.5 months for leader in the Text Mining and Knowledge Extraction AI domains: DataStork-AI-Case-Study

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Case Study: Large Scale Software Engineering

In Cloud proportions proven software patterns become bottlenecks, suboptimal decisions become disasters. The data layer is critical in distributed Cloud systems. See how DataStork utilized its experience in game-changing enterprise projects to engineer large scale social network architecture for world-wide

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Case Study: BigData for Logistics Optimizations

For Logistic companies to reduce delivery times and expenses is a way to survive. Enterprise Logistics may require massive computing power and still discover the trends after days of data crunching. Learn how DataStork helped a leading Logistics-Optimization company to

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