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Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.
About us
Innowise is an international full-cycle software development company founded in 2007. We are a team of 1400+ IT professionals developing software for other professionals worldwide.

Data management optimization: 20% reduction in data storage costs

Innowise has built a corporate data warehouse, automated ETL processes, and visualized data for enhanced data analytics in telecom industry.


Client since

Our client is a leading European telecom company. With a strong presence in the market, they cater to a vast user base, ensuring consistent communication services. 

Detailed information about the client cannot be disclosed under the provisions of the NDA.

Overcoming business analytics challenges with data management services

As technology advanced and data sources expanded, our client found themselves ensnared in a maze of unorganized data. The primary challenge was the lack of a unified system to aggregate and analyze data from various sources, hampering strategic planning and decision-making capabilities.
Additional issues included:

  • Slow data access. The time taken to retrieve and process data was lengthy, leading to operational inefficiencies and downtime.
  • Costly storage solutions. Data storage was not optimized, resulting in inflated costs.
  • Manual ETL processes. The extraction, transformation, and loading (ETL) of data was manual, making it cumbersome and prone to errors.
  • Inadequate reporting mechanisms. The existing reports and dashboards weren’t interactive and insightful enough to facilitate informed decision-making.
With these challenges in mind, the client approached Innowise for our unmatched data management services, expecting a robust and scalable system to overhaul their data analytics processes.

Comprehensive ETL services and data warehousing services for a telecom company

Understanding the challenges faced by the client, we embarked on an in-depth analysis to grasp the full spectrum of their needs. The client's ecosystem comprised disparate data sources, each containing valuable insights that remained untapped due to the lack of integration. To bridge this gap, we proposed creating a comprehensive data management system within our ETL services. This system was designed to integrate diverse data sources seamlessly, as well as refine and structure data, ensuring its readiness for analytics and decision-making.

Data collection and integration

Using Python scripts and Apache Spark’s distributed data processing capabilities, we ingested data from various sources such as relational databases, NoSQL stores, and file systems. This allowed our developers for a unified data landscape, facilitating accessibility for subsequent ETL processes and analytics.
Software optimization_ Chart_1

ETL services with Apache Airflow

Based on our experience with large-scale data processing, we chose Apache Airflow for orchestrating customers’ ETL pipelines. Using Apache Airflow’s dynamic workflow, we streamlined the extraction, transformation, and loading of data, ensuring consistency and removing potential discrepancies before storing data in the warehouse.

Data warehousing services with snowflake

Snowflake emerged as the top contender after testing various data warehousing solutions for its ability to handle large datasets and concurrent processing capabilities. We built a corporate data warehouse that ensured data was stored and retrieved with unprecedented speed, fulfilling one of the client’s primary needs.

Empowering analytics with Tableau implementation services

The client’s requirements revealed that visualization was a crucial aspect they were missing. Among various BI tools, Tableau was the clear winner for this project. Using data from Snowflake, we developed a module with interactive dashboards that continuously give the client’s employees deep insights and enable them to interact with and dissect data in many ways, fostering a data-driven environment.

Data processing automation

Automation is a must in today’s fast-paced business environment. Using Apache Airflow, we automated and scheduled data refreshes in the warehouse so that the client has access to real-time information without requiring manual triggers.

Data security

Our team fortified the data residing across the customer’s storages. We integrated advanced authentication and authorization protocols and employed encryption mechanisms, ensuring data sanctity and security at all times.

Technologies & tools

Data processing
Python, Apache Spark, Apache Airflow
MySQL, MongoDB, Snowflake
Visualization tools
Continuous deployment
Docker, Jenkins
Configuration management
Revision control systems
Network management systems
Server monitoring
Grafana, Prometheus
Log management
ELK Stack (Elasticsearch, Logstash, Kibana)
Jira, Selenium


Drawing from our experience, we have crafted and fine-tuned a structured workflow for ETL services and data warehousing services tailored to our client’s needs. This approach, distilled into strategic phases, ensured a smooth progression from start to finish. 


Discovery phase

Prior to diving into the technical details, we worked with our client to understand their challenges with data management. This allowed us to grasp their pain points and helped us align our visions. By the end of this phase, we had a clear Vision and Scope document outlining the project's roadmap.

Data integration phase

Following the discovery phase, our main priority was to unify the fragmented data landscape. By integrating data from a variety of sources, we constructed a cohesive environment where every piece of data found its rightful place.

ETL development phase

After integrating data, it was necessary to ensure smooth data flow. We designed robust ETL pipelines, transforming raw data into actionable insights. With meticulous testing and refinement, we succeeded in automating and enhancing the ETL process.

Data warehousing

With our ETL pipelines in place, we needed a powerhouse to store client’s data. By leveraging Snowflake capabilities, we provided a scalable storage that ensured swift data retrieval.

UI/UX design and dashboard development

Based on the warehouse data, we crafted user-friendly Tableau dashboards. Thanks to comprehensible designs, we ensured that the information was accessible and easily digestible for end users.

Testing and implementation

As we approached the finish line, we concentrated on resolving issues and implementing the solution. As a result of iterations and feedback, we developed a fully functional data management system. We adhered to the Agile methodology throughout the process, ensuring flexibility and responsiveness. Our primary channels for client communication included Slack and Zoom, while Jira served as a convenient tool for tracking and managing tasks.


Data Architect
Data Engineers
Project Manager
Business Analyst
BI Developer
QA Engineers
Database Administrator
DevOps Engineers
Security Analyst

Results: advanced data analytics in telecom industry and 20% reduction in data storage costs

Our solution had a transformative impact on the client’s operations and data analytics in telecom industry:

  • Rapid data access. As a result of Snowflake’s capabilities, data access time was reduced to 5 seconds.
  • Cost optimization. We achieved a 20% reduction in data storage costs, leading to significant savings.
  • Enhanced performance. The creation of interactive Tableau reports boosted employee efficiency and productivity.
  • Seamless automation. Implementing Apache Airflow for ETL processes eliminated manual tasks and streamlined operations.
  • Improved decision-making. With better reports and analytics, decision-making accuracy surged by 30%.

Through enhanced data management, we addressed the client’s concerns about data fragmentation and laid a foundation for future expansions and integrations, ensuring they remain at the forefront in terms of data analytics and management.

Project duration
  • January 2022 - January 2023


reduction in data storage costs


increase in decision-making accuracy

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