Streamline your ETL Process with Sourcetable

Sourcetable simplifies the ETL process by automatically syncing your live Factset data from a variety of apps or databases.

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    Extract, Transform, Load (ETL) is a fundamental process for managing the complex data ecosystem of FactSet, a leading financial data and software company. ETL enables the efficient handling of data from diverse sources, ensuring that it is cleansed, coherent, and readily accessible for analysis and reporting. The integration of ETL tools into FactSet's workflow is particularly valuable when loading data into spreadsheets for financial analysis, as it facilitates the seamless alignment of various data structures and enhances the accuracy and speed of data-driven decisions. On this page, we will delve into the intricacies of FactSet, explore a range of ETL tools tailored for FactSet data, and discuss the numerous use cases for employing ETL with FactSet data, which is pivotal for data analytics and data science. Additionally, we will introduce an alternative to the traditional ETL process for FactSet using Sourcetable, a platform that simplifies data consolidation. This will be complemented by a comprehensive Q&A section to address common inquiries regarding the ETL process with FactSet data, ensuring that users have a thorough understanding of the benefits and applications of ETL in the context of financial data management.

    What is FactSet?

    FactSet is a financial data and software company established in 1978 by Howard Wille and Charles Snyder. It operates as a comprehensive service provider catering to the needs of Wall Street professionals and individual investors by combining data from 317 independent data providers. FactSet's core function is to supply its users with valuable market analytics, financial content, stock screening, and customized data analytics.

    Headquartered in Connecticut, FactSet extends its services globally with 37 offices across 20 countries, and is structured into three business units to effectively serve clients in the United States, Europe, and Asia-Pacific regions. The company's dedication to quality service is reflected in its high client retention rate of 95%. Under the leadership of CEO Philip Snow, since 2015, FactSet continues to support over 200,000 users from more than 8,000 companies and organizations, including collaboration with over 130 partners.

    Despite the competitive landscape with rivals such as Morningstar, S&P Global, and Bloomberg, FactSet maintains its position as a leading software tool in the financial industry by offering a suite of specialized tools and services that enable proficient investment research and analysis.

    ETL Tools for FactSet

    FactSet's Enterprise Solutions team utilizes Databricks for the ingestion of batch and streaming data sources within their ETL processes. The core component of this system is the Databricks Lakehouse, which integrates various FactSet products and services. This Lakehouse architecture is pivotal in facilitating data-driven decision-making and advanced analytics for FactSet's clientele.

    The introduction of the Databricks Lakehouse represents a cost-efficient evolution from FactSet's prior solutions, significantly simplifying the architecture that was based on Azure Synapse. With the adoption of the Lakehouse, FactSet has seen remarkable improvements in ETL efficiency, cutting costs by nearly six times and achieving a performance speed that is 90 times faster than its predecessor. The Lakehouse leverages the Delta Lake table format to manage a wide spectrum of data, including raw, landing zone, transformation step, and warehousing data. The format is enhanced with features like auto-optimization, time travel, and schema evolution, which are particularly beneficial for ETL operations.

    Complementing these advancements, FactSet's ETL software has been geared towards optimizing data delivery. The use of object storage is a significant leap forward, allowing the delivery of diverse file types and serving as a modern, cloud-native method to phase out older technologies like S/FTP. This approach not only aids financial firms in reducing their reliance on traditional ETL processes but also improves data availability through features like timely alerts, audit trails, and a flexible data lake structure.

    When it comes to the broader landscape of ETL tools suitable for financial data, a myriad of options are available, each with its own set of features and capabilities. These tools, which include Informatica PowerCenter, Apache Airflow, and IBM Infosphere Datastage, among others, offer connectivity to a variety of data sources and destinations. The selection of an appropriate ETL tool for a financial firm hinges on factors like customizability, technical expertise requirements, and the costs associated with the tool, its infrastructure, and the necessary human resources.

    Sourcetable Integration

    Streamline Your ETL Process with Sourcetable

    When dealing with data from FactSet, the complexities of extracting, transforming, and loading (ETL) data can be a significant hurdle. Sourcetable offers a streamlined and intuitive solution for professionals seeking to bypass the intricacies of using a third-party ETL tool or the challenges of developing an in-house ETL system. By leveraging Sourcetable, you can effortlessly sync your live data from FactSet, alongside various other apps or databases, directly into a user-friendly spreadsheet interface.

    One of the primary benefits of choosing Sourcetable for your ETL needs is its automation capabilities, which can greatly reduce the time and effort required to consolidate your data. The familiar spreadsheet interface simplifies the querying and manipulation of data, making it accessible even to those with limited technical expertise. This immediacy and ease of use are what set Sourcetable apart, allowing you to focus on deriving actionable business intelligence without the overhead of managing complex ETL processes.

    Common Use Cases

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      Sourcetable Integration
      Gathering large amounts of data for a thesis
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      Sourcetable Integration
      Analyzing financial data in Excel for research
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      Sourcetable Integration
      Consolidating diverse datasets for easier access and manipulation in Excel

    Frequently Asked Questions

    What is the role of Databricks in FactSet's ETL processes?

    Databricks is used by FactSet to ingest both batch and streaming sources, and it is also utilized within FactSet's Lakehouse for ETL (Extract, Transform, Load) operations.

    What is FactSet's Lakehouse and what are its capabilities?

    FactSet's Lakehouse processes and exposes data across customer platforms, is used for analytics, and incorporates Databricks' Lakehouse model, which unifies data lake assets with warehouse-level performance and includes unified governance and scalable tools.

    How does FactSet's Lakehouse model benefit from Databricks?

    FactSet's Lakehouse benefits from Databricks through the unified data lake and data warehouse architecture, which enhances performance, governance, and scalability for analytics and ETL tasks.

    Are there any other common ETL tools used besides Databricks?

    Yes, there are many other ETL tools such as Informatica PowerCenter, Apache Airflow, and Microsoft SQL Server Integration Services (SSIS), among others, that can connect to various data sources, automate data integration processes, and transform and load data efficiently.

    Why is it important to consider the cost of infrastructure and human resources when choosing an ETL tool?

    The long-term maintenance cost of infrastructure and human resources is a critical factor in choosing an ETL tool because it impacts the total cost of ownership and the sustainability of the data integration solution.


    In summary, FactSet's integration of Databricks has revolutionized its ETL process, offering a significant cost reduction and boosting performance, underpinned by a robust Lakehouse that encourages data-driven decision making. The seamless connection to Spark Delta Lake through Databricks enables efficient data handling and advanced features like time travel, schema evolution, and governance. ETL tools, essential for extracting, transforming, and loading data, not only simplify data migration but also ensure data quality and compliance, catering to the needs of various business scales with their flexible, scalable, and secure architecture. Instead of navigating the complexities of ETL tools, consider Sourcetable for a streamlined ETL process into spreadsheets. Sign up for Sourcetable today to get started and transform your data management strategy.

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