Agreeable Data

In today's data-driven world, organizations need a comprehensive technology stack to effectively collect, process, analyze, and leverage their data. This guide explores the key components of a modern data stack and how they work together to create a robust data ecosystem.

1. Generic Data Capture / Event Streaming

These tools are used to capture data from your website or app. These tools capture page views, clicks, marketing attribution information, and much more. These tools can also be used in place of google tag manager – which can drastically speed up your website's loading time. You get speed back because the data gets pulled once by the data capture tool and then distributed to multiple downstream tools/systems. Some examples of downstream tools would be: hotjar, luckyorange, hubspot, google ads, google analytics, optimizely, klaviyo, mailchimp, and many more.

Popular Event Stream / Data Capture Tools

Segment.io: A customer data platform (CDP) that helps you collect, clean, and control your customer data. It provides a single view of your customer and enables you to send data to hundreds of tools with the flip of a switch.
Snowplow.io: An open-source behavioral data collection platform that gives you complete ownership of your data. It's particularly useful for organizations that need granular control over their data collection and processing.
RudderStack: An open-source CDP that helps you collect, transform, and route your customer data to various destinations. It's particularly popular among organizations that want to maintain data ownership while leveraging modern data tools.

2. Data Extraction (ETL/ELT)

Data extraction tools are crucial for moving data from various sources into your data warehouse. Modern tools have shifted from traditional ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform) approaches, which offer more flexibility and better performance.

Popular Data Extraction Tools

Fivetran: A fully managed, automated data pipeline that connects to hundreds of data sources. It's known for its reliability, ease of use, and extensive connector library.
Airbyte: An open-source data integration platform that allows you to create custom connectors and maintain full control over your data pipelines. It's particularly useful for organizations with unique data sources or specific requirements.

3. Data Storage

Modern data storage solutions offer scalability, performance, and cost-effectiveness that traditional databases can't match. These solutions are designed to handle massive amounts of data while providing fast query performance.

4. Data Syndication (Reverse ETL/ELT)

Reverse ETL tools help you move data from your data warehouse back into operational systems, enabling data-driven decision-making across your organization.

Popular Data Syndication Tools

Census: A reverse ETL platform that helps you sync your warehouse data with operational tools. It's particularly useful for organizations that want to leverage their data warehouse as a single source of truth.
Hightouch: A reverse ETL platform that enables you to sync data from your warehouse to various operational tools. It's known for its ease of use and extensive connector library.

5. Data Analysis & Visualization

These tools help you transform raw data into actionable insights through analysis and visualization.

Popular Analysis & Visualization Tools

Mode Analytics: A collaborative analytics platform that combines SQL, Python, and visualization capabilities. It's particularly well-suited for data teams that need to work together on complex analyses.
Hex: A modern data workspace that combines SQL, Python, and visualization in a collaborative environment. It's known for its interactive notebooks and real-time collaboration features.
Tableau: A powerful visualization tool that helps you create interactive dashboards and reports. It's particularly well-suited for organizations that need to share insights with non-technical stakeholders.
Looker/Google Data Studio: A business intelligence platform that enables you to explore, analyze, and share real-time business analytics. It's particularly well-integrated with Google's ecosystem.

6. Data Machine Learning / AI

Modern AI/ML tools make it easier than ever to leverage machine learning for business insights and automation.

Popular AI/ML Tools

Obviously.ai: An automated machine learning platform that helps you build and deploy ML models without extensive coding. It's particularly well-suited for organizations that want to quickly implement predictive analytics.
Continual.ai: A platform that helps you build and deploy ML models directly in your data warehouse. It's known for its ability to handle large-scale data and complex ML workflows.
Akkio: A no-code AI platform that helps you build and deploy machine learning models quickly. It's particularly well-suited for organizations that want to implement AI without extensive technical expertise.

Conclusion

Building a modern data stack involves carefully selecting and integrating various tools that work together to create a comprehensive data ecosystem. The key is to choose tools that not only meet your current needs but also scale with your organization as it grows. Remember that the best stack is the one that enables your team to work efficiently while delivering value to your organization.

When building your stack, consider factors such as:

Your team's technical expertise
Your organization's data volume and velocity
Your budget constraints
Your security and compliance requirements
Your existing technology ecosystem

By carefully selecting and integrating these tools, you can create a data stack that enables your organization to make data-driven decisions effectively and efficiently.

Modern Data Technology Stack: A Comprehensive Guide