According to IDS, a premier market intelligence provider, the global data volume that has grown up to 40 zettabytes in 2020 will reach 175 zettabytes by 2025! Is your business ready to benefit from analyzing the ever-increasing data volumes?
ScienceSoft: Your Big Data Companion
Founded in 1989, ScienceSoft is an international IT consulting and custom software development company headquartered in McKinney, Texas, USA.
More than 700 IT professionals, located internationally, offer consulting services, and bring custom and platform-based solutions to large and mid-sized companies in healthcare, banking, retail, telecoms, and other industries. To ensure that the company’s clients benefit from steady reliability and originality across solutions, ScienceSoft partners with Microsoft, Amazon Web Services, IBM, Oracle, and other technology leaders.
With 31 years in data analytics and data science, ScienceSoft has been delivering big data services since 2013. Alex Bekker, Head of Data Analytics Department at ScienceSoft, says: “For more than 7 years, we have been helping businesses gain control over their big data environment by providing a full range of big data services: consulting, implementation, support, and big data managed analytics services.”
Big data consulting
To help business implement big data solutions or overcome the hurdles that restrain them from fully leveraging big data capabilities, ScienceSoft renders big data implementation and improvement consulting services that include:
- Detailed roadmaps to harnessing big data capabilities.
- Recommendations for data quality management.
- Implementation strategies.
- User adoption strategies.
- Evolution strategies.
- Recommendations for a solution’s architecture, etc.
“While rendering our consulting services, we consider all the existing technologies in the big data universe, so our clients can effectively derive sense out of vast data volumes and maximize their analytics potential,” says Alex.
Big data implementation
To meet diverse analytical needs of the business, ScienceSoft implements big data solutions with some or all of the following architecture components: a data lake, a data warehouse, ETL processes, OLAP cubes, reports, and dashboards. Additionally, ScienceSoft sets up data quality management and data security practices, trains, and applies machine learning models.
“Our big data implementation offering is aimed at assisting companies within the whole life cycle of a big data solution, from requirements elicitation and defining the big data implementation strategy to the deployment and after-launch support,” explains Alex.
Big data support
ScienceSoft is ready to support its clients’ big data solution by offering administration (updating software, adding new users, and handling permissions) and data administration procedures (data cleaning, backup, and recovery). Besides, to ensure security, failure-resistance, and high performance of the big data solution, ScienceSoft conducts regular health checks or monitors the solution on a permanent basis to identify problems early and troubleshoot them in as short a time as possible.
Big data managed analytics services
ScienceSoft renders managed analytics services to meet the demand of the companies, which want to quickly derive insights out of their big data and focus on their core business activities without developing and managing a full-scale big data solution.
The point of the service is that our clients access the required analytics insights on a subscription fee basis, while we are fully responsible for the quality of those insights,” explains Alex.
ScienceSoft Helps with Big Data Implementation for Advertising Channel Analysis in 10+ Countries
A leading market research company was going to upgrade its robust analytical system to cope with the continuously growing amount of data and enable quick and comprehensive advertising channel analysis. The company was searching for an experienced team to implement the project and assigned ScienceSoft to fulfill the entire migration.
For the new analytical system, the selected framework included Apache Hadoop for data storage, Apache Hive for data aggregation, query and analysis, and Apache Spark for data processing. Amazon Web Services and Microsoft Azure were selected as cloud computing platforms.
The new system consisted of five modules: data preparation, staging, data warehouse 1 and 2, and a desktop application. During the migration, the old and the new systems were operating in parallel,” explains Alex.
The new system was supplied with raw data taken from multiple sources. To enable the system to process more than 1,000 different types of data, data preparation included the four stages coded in Python: data transformation, data parsing, data merging, and data loading. Apache Hive formed the core of the staging module.
To guarantee data processing on the fly, the first and the second data warehouses were based on Apache Hive and Apache Hive + Apache Spark with the ETL blocks written in Python and Scala accordingly.
The desktop application was developed with the .NET framework, and it allowed creating standards as well as ad-hoc reports in the easy-to-understand charts.
“With the delivered solution our customer could process queries up to 100 times faster, conduct cross-analysis of almost 30,000 attributes and build intersection matrices allowing multi-angled data analytics for different markets,” adds Alex.
Alex Bekker, Head of Data Analytics Department at ScienceSoft
Alex has been in charge of ScienceSoft’s Data Analytics Department since 2010. Under his leadership, the department started big data consulting and implementation practice and extended the service portfolio with data science, machine, and deep learning. Being a huge fan of advanced big data technologies, Alex guides companies to optimized business operations, sales effectiveness, personalized customer experience, and accurate predictions.
Conclusion
ScienceSoft’s multifaceted IT experience of 31 years helps companies satisfy their diverse analytical needs with comprehensive solutions.
“We feel very passionate about big data and data analytics as a whole, and are ready to help companies create value with big data potential,” concludes Alex.
Headquarters/Location: McKinney, Texas, the United States of America