Learn
/
Data Engineering
September 16, 2024

Significance of data engineering tools in the banking sector

Explore how data engineering tools like Apache Spark, Tableau, and Secoda are revolutionizing the banking industry by automating analytics, streamlining processes, and aiding in informed decision-making.
Dexter Chu
Product Marketing

Get started with Secoda

See why hundreds of industry leaders trust Secoda to unlock their data's full potential.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

How to evaluate a data catalog

See why hundreds of industry leaders trust Secoda to unlock their data's full potential.

Get the guide

Why are data engineering tools important for the banking industry?

Data engineering tools are important for the banking industry as they help in automating analytics, streamlining event processing, conducting analytics, and securely storing data streams. These tools also aid in data visualization, analysis, and transformation, which are crucial for making informed business decisions. Furthermore, they provide a secure development environment for ETL tasks, making them indispensable for the banking industry.

  • Data engineering tools help in automating analytics, streamlining event processing, and securely storing data streams.
  • These tools aid in data visualization, analysis, and transformation, which are crucial for making informed business decisions.
  • They provide a secure development environment for ETL tasks, making them indispensable for the banking industry.

What are some data engineering tools used in the banking industry?

Data engineering tools are crucial for the banking industry as they help in automating analytics, streamlining event processing, conducting analytics, and securely storing data streams. Some of these tools include Apache Spark, Apache Kafka, Amazon Redshift, Tableau, Power BI, Apache Hive, BigQuery, Dbt.data, Python, and Secoda.

  • Apache Spark: Uses machine learning to automate analytics by accessing customer data from multiple repositories, correlating it into a single file, and sending it to marketing.
  • Apache Kafka: An open-source tool that helps businesses streamline event stream processing and conduct analytics. It allows IT teams to securely store data streams in clusters and connect clusters across different geographic regions.
  • Amazon Redshift: A cloud-based data warehousing service from Amazon Web Services (AWS) that uses massively parallel processing (MPP) architecture to allow data engineers to quickly analyze large datasets.
  • Secoda: A data discovery and cataloging tool that centralizes data knowledge, making it easier for banks to manage, discover, and trust their data. It helps streamline data governance and improve collaboration among teams.

How can Tableau and Power BI be used in the banking industry?

Tableau and Power BI are powerful data visualization and analysis tools that can be used in the banking industry. Tableau provides interactive data visualization tools for business intelligence and analytics. Banks can use Tableau to track the value of investments and savings, analyze data, monitor risks, and create reports. Power BI is a data analysis tool from Microsoft that allows banks to collect data from multiple sources and measure their performance on a single platform.

  • Tableau: Provides interactive data visualization tools for business intelligence and analytics. Banks can use Tableau to track the value of investments and savings, analyze data, monitor risks, and create reports.
  • Power BI: A data analysis tool from Microsoft that allows banks to collect data from multiple sources and measure their performance on a single platform. Consulting partners can help banks use Power BI to track KPIs and analyze the success of their financial operations.

What are the benefits of using Apache Hive and BigQuery in the banking industry?

Apache Hive and BigQuery are valuable tools for big data analytics and data warehousing in the banking industry. Apache Hive provides an SQL-like interface for working with big data, and is scalable enough to handle large data sets. BigQuery is a fully managed tool from Google that supports data analysis, machine learning algorithms, geospatial analysis, and business intelligence solutions.

  • Apache Hive: Provides an SQL-like interface for working with big data, and is scalable enough to handle large data sets. It's a valuable tool for big data analytics and data warehousing.
  • BigQuery: A fully managed tool from Google that supports data analysis, machine learning algorithms, geospatial analysis, and business intelligence solutions.

How does Dbt.data and Python aid in data engineering in the banking sector?

Dbt.data and Python are essential tools for data engineering in the banking sector. Dbt.data allows users to model, transform, and deploy their data warehouse. It provides a secure development environment for Extract, Transform, and Load (ETL) tasks. Python, on the other hand, has a large ecosystem of libraries, such as Pandas and NumPy, that make it ideal for data analysis, transformation, and manipulation.

  • Dbt.data: Allows users to model, transform, and deploy their data warehouse. It provides a secure development environment for Extract, Transform, and Load (ETL) tasks.
  • Python: Has a large ecosystem of libraries, such as Pandas and NumPy, that make it ideal for data analysis, transformation, and manipulation.

How can Secoda benefit the banking industry?

Secoda is a data discovery and cataloging tool designed to centralize data knowledge, making it easier for banks to manage, discover, and trust their data. By integrating with various data sources, Secoda helps streamline data governance and improve collaboration among teams. This leads to more efficient data management, enhanced data quality, and better decision-making processes within the banking industry.

  • Centralizes data knowledge, making it easier to manage and discover data.
  • Streamlines data governance, improving data quality and compliance.
  • Enhances collaboration among teams, leading to more efficient data management and better decision-making.
In this story
This is also a heading
This is a heading

Keep reading

View all
Data Strategy and Leadership

Best Data Marketplace Tools for 2025

Discover the top data marketplace tools for 2025. Compare features, benefits, and insights to optimize your data strategy.

Data Monitoring and Observability

5 Things Every Data Engineer Should Know About Data Observability and Monitoring

Discover the essentials of data observability and monitoring for data engineers, including key metrics, tools, automation, and proactive strategies.

Data Monitoring and Observability

What is a Data Observability Framework?

Learn about a data observability framework, its components, benefits, and best practices to ensure reliable, high-quality, and compliant data-driven operations.

Get started in minutes

Built for data teams, designed for everyone so you can get more from your data stack.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
CustomersCareersDocumentationGlossary

Product

Data catalogData dictionaryData lineageData sharingData ticketingData governanceData privacyData observability

Solutions

Data LeadData EngineerData AnalystData ConsumersGovernance ManagerBusiness OperationsProduct Managers

Use cases

Metadata managementData onboardingData enablementData documentationSelf service business intelligence

Resources

PricingCustomersBlogGuidesDocumentationBuyers guideSecoda vs. AtlanSecoda vs. Open SourceSecoda vs. CastorSecoda vs. Select StarSecoda vs. CollibraROI calculatorData enablement whitepaperExplore

Company

AboutContact usCareersGDPR readinessSecurityBrand

Social

LinkedInXInstagramYoutube

© 2025 Secoda, Inc. All rights reserved.
By using this website, you accept our Terms of Use and Privacy Policy.