Data lake & streamlining data extraction

Synopsis

An energy solutions manufacturer implemented a comprehensive data extraction and transformation solution to streamline operations, reduce costs, and improve data reliability. By using Fivetran, Snowflake, DBT, and Power BI, the integrated system has automated processes, improved scalability, and empowered non-technical users to make informed decisions to position the company for strategic growth.

‍

Key figures

Customer: Global manufacturer of energy solutions

Project volume: 40,000 USD, 3 months

‍

Project scope

A manufacturer and designer of energy solutions recognized the need for a comprehensive solution to streamline their data extraction process from multiple sources and seamlessly transfer the data to a data warehouse.

This transformation was essential to facilitate essential processes such as normalizations and calculations and ultimately enable clean data to be used in a data visualization tool. The decision to implement this solution was driven by several critical factors:1 Diverse data sources: The existence of multiple data sources, including ERP, analytics API, API from external partners, and databases, required specialized developer skills for data extraction.

In addition, this approach caused additional extraction costs, particularly when working with different APIs and databases.2 Data differences: The various types of data in different formats combined with different identifiers and entity names required transformation and normalization to ensure the consistency and coherence of the entire data set.3 Manual processes: The existing workflow included manual intervention to import clean data into the data visualization tool, which was not It only cost time, but also entailed the possibility of mistakes.

The overarching goal was to achieve the following improvements:

Cost and Effort Optimization: By introducing a more streamlined process, the aim was to reduce both human and machine costs associated with data extraction and manipulation.
Automation of data processes: The implementation sought to automate the transfer and transformation of data, eliminating the need for manual interventions and enhancing overall efficiency.
Accessibility for Non-Technical Users: The objective was to establish a sustainable system that could be effectively managed by non-technical personnel, ensuring long-term viability and ease of operation

The overall goal was to achieve the following improvements:

optimization of costs and effort: By introducing a leaner process, the costs associated with data extraction and manipulation for humans and machines should be reduced.
Automating data processes: The implementation was aimed at automating the transfer and transformation of data, eliminating the need for manual intervention and increasing overall efficiency.
Accessibility for non-technical users: The aim was to create a sustainable system that can also be effectively managed by non-technical personnel to ensure long-term viability and simple operation.

‍

Implementation

‍

The implemented solution included the introduction of an Extract, Load, Transform (ELT) approach using Fivetran as a data integration tool, Snowflake as a data warehouse, and DBT (Data Build Tool) for advanced data transformation. Fivetran played a critical role in automating data extraction from various sources, including ERP, analytics API, external partner API, and databases, simplifying the process and minimizing manual effort.

Snowflake's scalability and performance features played a decisive role in efficiently processing the various data sets. In addition, the integration of DBT facilitated the necessary transformations, normalizations, and calculations of the raw data, ensuring the consistency and coherence of the entire data set. Power BI was used for data visualization, which provides an easy-to-use interface for non-technical users to explore and understand the insights gained from the transformed and purified data.

The combination of Fivetran, Snowflake, DBT, and Power BI created a seamless end-to-end solution that streamlines the ELT process, improves automation, and enables non-technical stakeholders to derive meaningful insights from the data. The implementation of this integrated solution resulted in significant gains in various dimensions of the company's data management and analytics processes.

‍

Use case: Inverter availability and identification of data gaps

An important use case within the implemented solution involves monitoring inverter availability, a critical aspect in the area of energy solutions. Data retrieval is occasionally delayed due to communication problems, which leads to gaps in the data series. To address this challenge, a solution was developed to identify these data gaps: The system is designed to identify cases where inverter data is not available during the planned retrieval.

The solution uses the advanced features of DBT (Data Build Tool) to intelligently identify these gaps and trigger an automated process to retrieve the missing data from the source. The backfilling mechanism ensures seamless continuity in data series, mitigates the effects of temporary unavailability, and maintains the integrity of the entire data set.

‍

Profits through the integrated data solution

First, the introduction of an ELT approach supported by Fivetran, Snowflake, and DBT resulted in significant time and cost savings. The automated processes for data extraction and transformation reduced dependency on manual intervention and thus optimized the workload for humans and machines.

Using Snowflake as a data warehouse provided scalability and performance improvements as the various data sets could be processed seamlessly. This scalability resulted in improved processing speed and efficiency, which in turn contributed to improved overall system performance. DBT played a key role in ensuring data consistency and reliability. The ability to transform and normalize data streamlined operations and provided more accurate and reliable insights.

With Power BI as the chosen data visualization tool, even non-technical users had access to a user-friendly interface that enabled them to explore and interpret data insights on their own. This not only improved decision making but also fostered a culture of data-driven decision support across the organization.

Essentially, the gains included operational efficiency, cost reduction, improved data reliability, and improved accessibility for non-technical users, which enabled the organization overall to make more informed and strategic decisions.