Chen Cuello
SEP 29, 2023
5 min read
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With streams of data flooding in from various channels, it’s easy to feel overwhelmed. Does it look like you are literally drawing in data? You might not be exaggerating. According to a recent survey:

  • Data volumes in organizations surge by 63% on average each month.
  • A whopping 10% of companies see their data volumes double monthly.
  • The typical organization taps into 400 distinct data sources.
  • Over 20% of surveyed companies harness the power of 1,000 or more data sources for their analytics and BI systems.

More and more, businesses come to realize that the value of data lies not in its volume but in the insights you can derive from it in practice. Enter the Modern Data Stack (MDS)—a dynamic set of tools and technologies used to collect, store, process, and analyze data in an efficient and scalable manner.

What is a Modern Data Stack?

Data Stack is an integrated suite of tools, workflows, and processes that facilitate end-to-end data management – from collection to analytics. Data stack is like the central nervous system of a data-driven organization, ensuring that data is effectively captured, processed, and utilized to drive value.

The significance of a well-structured MDS lies in its capacity to streamline data workflows, ensure data quality, and empower organizations to extract actionable insights from the vast oceans of data all around them.

Key Categories of Tools in a Modern Data Stack

To get a better understanding of the different components of a modern Data Stack, let’s take a closer look at some of the key tools that make up this ecosystem:

  1. Data Collection & Integration Tools: These tools ensure that data from diverse sources like mobile apps, websites, IoT devices, and third-party applications can be ingested seamlessly into the data stack.
  2. Cloud Data Warehouses: Unlike traditional databases, cloud data warehouses like Snowflake, BigQuery, and Redshift are designed for large-scale, high-speed, and flexible data storage solutions.
  3. Data Transformation & Modeling: Once data is ingested, it’s often raw and unstructured. Tools like dbt help transform this raw data into a more structured and usable format, making it ready for analysis.
  4. Analytics & Business Intelligence: Tools like Looker, Tableau, and Power BI enable businesses to derive actionable insights from their data, transforming numbers and charts into strategies and decisions.
  5. Data Orchestration & Workflow Automation: As data flows through the organization, it’s essential to automate and coordinate these workflows. Solutions like Apache Airflow or Prefect help streamline these processes.

Data Collection & Pipelining: The Cornerstones of Effective Data Management

At the foundation of any data stack lies the toolkit for collecting raw data from various sources and moving it to centralized data storage. This process, while sounding straightforward, is complex, given the variety of data formats, sources, and eventual use cases. That’s where data pipelines come into the picture.

Data Pipelines

Data pipelines form the backbone of any robust data infrastructure. These are automated processes that move data from one location to another – usually from various sources to a central repository. Think of them as the highways of data, enabling a swift, seamless, and structured flow of information. Their primary role in data integration is to ensure consistency, accuracy, and availability of data, ensuring data from various sources is standardized and made ready for analysis.

Rivery: As a data integration and orchestration tool, Rivery simplifies data collection, ensuring that data from different sources is seamlessly integrated into a data warehouse. Its easy-to-use interface, adaptability, scalability, and compatibility with numerous data sources make it a critical tool in the Modern Data Stack. With Rivery, you can:

  • Integrate data from multiple sources effortlessly, whether it’s from traditional databases, cloud platforms, or SaaS applications.
  • Transform this data on the fly, ensuring it aligns with the destination’s schema and the organization’s requirements.
  • Orchestrate complex workflows, ensuring data processes are automated, reliable, and efficient.

Twilio Segment (Connections): While River offers a broad spectrum of data integration capabilities, tools like Twilio Segment specialize in event data collection and integration. The segment provides a unified API for teams to track customer data accurately across every touchpoint, be it websites, mobile apps, servers, or cloud applications. It then routes this data to any analytics or marketing tool, ensuring that teams have a consistent, holistic view of their customers.

Key Takeaway: whether it’s the broad integration capabilities of Rivery or the specialized focus of Twilio Segment, data collection and pipelining tools are pivotal. They ensure that data transforms from chaos into a cohesive, integrated asset, driving business value at every turn.

Data Storage: The Foundation for Scalable & Flexible Data Solutions

Data storage is at the heart of any Data Stack. After all, without a secure and scalable repository to store data, you can’t do much with your data. That’s why cloud data warehouses have become central to many data stacks.

As data collection methods become more sophisticated, so too do the requirements for data storage. Traditional storage methods simply aren’t equipped to handle today’s vast amounts of data, its variety, and the speed at which it is generated. Enter the era of cloud data warehouses – the linchpin of modern data infrastructure.

Cloud Data Warehouses: Unlike traditional databases that were designed for transactional processes, cloud data warehouses are built for analytics. They offer fast query performance, scalability, and the ability to handle massive datasets with diverse structures. The primary advantage of cloud data warehouses is their flexibility; they can expand and contract based on demand, making them both cost-effective and performance-optimized.

Today, 37 percent of organizational data resides in cloud data warehouses, with 25 percent still stored in offsite, non-cloud data warehouses. We anticipate those numbers will shift significantly in the near future, with most organizations planning to migrate data to the cloud over the next two years.

BigQuery: Google’s BigQuery stands out in the world of managed cloud data warehouses. It’s serverless, highly scalable, and offers real-time analytics capabilities. Here’s what sets BigQuery apart:

  • Serverless Nature: With BigQuery, there’s no infrastructure to manage. This means businesses can focus on analyzing data without the overhead of maintaining a database.
  • Auto-scaling: It automatically adjusts resources based on the workload, ensuring that queries are processed at lightning speed, regardless of the dataset’s size.
  • Integrated Machine Learning Capabilities: Users can build and deploy machine learning models directly within BigQuery, using SQL. This seamless integration allows for quicker insights and predictive analytics.

Snowflake: Another titan in the managed cloud data warehouse domain is Snowflake. Built specifically for the cloud, Snowflake offers a unique architecture that separates storage, computation, and cloud services, allowing each to scale independently. Here’s where Snowflake shines:

  • Multi-Cloud Capability: Snowflake can operate on multiple clouds, including AWS, Azure, and Google Cloud, giving businesses flexibility and avoiding vendor lock-in
  • Dynamic Scaling: Whether there’s a need for larger computational resources for intensive analytics or a scale-down post-analysis, Snowflake can adjust on-the-fly, optimizing costs and performance.
  • Zero Maintenance: Snowflake handles all operational aspects, like patching, tuning, and backups, letting users concentrate solely on deriving value from their data

Key takeaway: as the data landscape grows in complexity and volume, the data stack’s foundation’s strength becomes paramount. Managed cloud data warehouses like BigQuery and Snowflake elevate the data storage game to new heights – they encapsulate what the future of data storage looks like: scalable, flexible, and powerful.

Data Transformation and Modeling: Shaping Data for Analysis

Raw data is only valuable if it can be transformed into actionable insights. That’s where the process of data transformation and modeling comes in. It involves converting raw, unstructured data into a structured format that can be analyzed and interpreted.

While traditional ETL tools have been used to perform this task, today’s Modern Data Stacks demand more advanced and agile solutions. These include tools like dbt and ELT.

dbt: dbt (data build tool) is an open-source data transformation tool that allows businesses to transform raw data into analytics-ready tables using SQL. It offers a powerful and easy-to-use workflow for modeling and structuring data:

  • SQL-Centric: dbt relies on the familiar SQL, enabling analysts to define, document, and execute data transformations without needing to lean on engineering. This democratizes the data transformation process and accelerates time-to-insight.
  • Version Control & Collaboration: With dbt, teams can manage transformations as code, incorporating best practices like version control, code reviews, and collaboration. This ensures consistent, maintainable, and high-quality data models.
  • Extensibility & Modularity: dbt models are modular, allowing analysts to build upon existing models, reusing logic, and ensuring a single source of truth. Additionally, its rich ecosystem of plugins and macros means it can be tailored to specific needs and workflows.
  • Full Lifecycle Management: From testing and documentation to deployment, dbt covers the entire lifecycle of data transformation and modeling. This holistic approach ensures reliability, accuracy, and transparency in data processes.

ELT: Unlike traditional ETL, which involves extracting data from various sources, transforming it within an ETL tool, and then loading it into a database for analysis, ELT (Extract, Load, Transform) takes the opposite approach. It involves loading raw data into the data warehouse first and then using tools like dbt to transform and model it for analysis:

  • Better Performance: In ELT, data is transformed and modeled within the database, leveraging its raw processing power. This results in faster and more efficient data processing.
  • Data Flexibility: ELT allows businesses to load all types of raw data into the warehouse without any pre-processing or transformation. This ensures that no valuable insights are lost due to restrictions on the data types that can be loaded.
  • Future-proofing: ELT future-proofs your data stack, as it allows for easy integration of new data sources and changes in business needs. This gives businesses the agility to adapt quickly to changing data environments.

Key takeaway: These tools empower data professionals to take charge of the transformation and modeling process. By facilitating a more agile, collaborative, and code-centric approach, data transformation tools ensures data is not only clean and consistent but also primed for analysis.

Identity Resolution & Customer Profiles: The Key to Personalized Engagement

With multiple touchpoints and varied digital identities, consolidating disparate pieces of user information into a coherent and singular customer profile is challenging, to say the least. Enter the domain of identity resolution and customer profile building.

Understanding Identity Resolution

Identity resolution is about connecting multiple identifiers across different channels and devices to a single individual or entity. This process is crucial, especially in marketing and e-commerce sectors, where understanding a customer’s journey is pivotal to effective engagement.

The Role of Customer Data in Personalization & Analytics

With a unified customer profile, businesses can create tailored experiences, be it product recommendations, personalized content, or targeted ads. Moreover, a consolidated view of the customer journey aids in more profound analytics, segmentation, and predictive modeling—equipping businesses with insights to drive retention, upsell opportunities, and overall user satisfaction.

Homegrown Identity-Resolution via dbt: While off-the-shelf solutions have their merits, there’s an increasing trend towards custom-built identity resolution frameworks, especially using tools like dbt. With dbt’s powerful transformation capabilities, data teams can:

  • Define custom logic to match and merge identities based on specific business rules.
  • Incorporate fuzzy matching techniques to account for minor discrepancies in data.
  • Leverage historical data and machine learning models to enhance the accuracy of identity resolution.

However, building a scalable, accurate, and comprehensive identity resolution system is no mean feat. It requires technical acumen, domain knowledge, and the right tools to handle the complexities of customer data.

Rivery:  As the world of data integration and orchestration evolves, turn-key tools like Rivery have emerged as invaluable assets in gaining full view of your customers:

  • Unified Data Streams: Rivery can pull data from various sources, whether it’s CRM systems, e-commerce platforms, social media channels, or even IoT devices. By consolidating these streams, it aids in building a 360-degree view of customers.
  • Real-time Data Integration: In the fast-paced digital landscape, timeliness is key. Rivery’s real-time data integration ensures that customer profiles are always up-to-date, capturing the latest interactions, preferences, and behaviors.
  • Data Quality Assurance: Rivery’s robust data quality checks ensure that the integrated customer data is accurate, consistent, and reliable—forming a solid foundation for identity resolution.

Analytics & BI: Powering Data-Driven Decision Making

Whether it’s predicting customer behaviors, streamlining operational inefficiencies, or identifying market trends, analytics is the name of the game. By deriving actionable insights from vast amounts of data, analytics equips businesses with a clearer understanding of their operations, customer base, and market, enabling them to navigate challenges and seize opportunities.

Business Intelligence tools complement analytics by offering visual, interactive ways to explore, analyze, and present data. Through dashboards, reports, and visualizations, BI tools make complex data narratives easily digestible, facilitating more informed, swift decision-making across all business levels.

Hex: The New Kid on the Block: One of the emerging stars in the analytics and BI space is Hex. Differentiating itself from traditional tools, Hex allows data scientists and analysts to build interactive data apps. These apps combine code, data, and visualizations into a collaborative interface, bridging the gap between data teams and business stakeholders. It ensures that insights aren’t just confined to analysts but are accessible and actionable across the organization.

Looker: A Modern BI Powerhouse: No discussion on BI tools would be complete without mentioning Looker. As one of the leaders in the BI space, Looker stands out for its:

  • Model-Based Approach: Looker uses a unique modeling language, LookML, allowing for a reusable, version-controlled data model. This ensures consistent metrics across the organization.
  • Self-Service Analytics: With its intuitive interface, Looker empowers even non-technical users to explore data, build dashboards, and derive insights.
  • Seamless Integrations: Looker plays well with modern data stacks. Whether it’s pulling data from cloud warehouses like BigQuery and Snowflake or pushing data to third-party apps, Looker offers a truly connected data experience..

Data Orchestration: Ensuring a Streamlined Modern Data Tech Stack

As data tech stacks have evolved, the complexities associated with managing them have risen to ridiculous levels. With multiple tools and data sources in play, the risk of disjointed processes and siloed data has increased dramatically.

Data orchestration is about coordinating various data tasks, from extraction, transformation, and loading (ETL) to more advanced analytics and machine learning operations. Think of it as the conductor that ensures each ‘instrument’ in your data stack plays the right note at the right time.

The Role of Data Orchestration in Automation: As data landscapes become more complex—with data sourced from myriad systems, processed in multiple stages, and served to diverse endpoints—manual management becomes too complex. Data Orchestration tools automate these processes, ensuring they’re streamlined, efficient, and error-free. They allow for:

  • Scheduled Workflows: Running specific data tasks at defined intervals, be it hourly data refreshes or monthly reports.
  • Dependency Management: Ensuring tasks run in the correct sequence, with subsequent tasks triggered only after the preceding ones are completed successfully.
  • Error Handling: Monitoring workflows and providing alerts for failures, often with the ability to retry tasks or execute fallback mechanisms.

Apache Airflow: The Gold Standard in Orchestration: One of the frontrunners in the world of data orchestration is Apache Airflow. Designed by Airbnb and later contributed to the Apache Foundation, Airflow boasts:

  • Dynamic Workflow Creation: Using Python, users can create workflows as code, allowing for dynamic pipeline generation and easy version control.
  • Rich UI: Airflow’s interface provides a clear view of task dependencies, progress, and logs—making it simpler to monitor and troubleshoot.
  • Extensibility: With its plugin architecture, Airflow can easily integrate with numerous data systems, from traditional databases to cloud platforms.

Dagster: The Next Wave in Workflow Automation: While Airflow has captured the attention of many, newer entrants like Dagster are making waves. Dagster focuses on:

  • Data Quality and Testing: It integrates testing and data quality checks into the orchestration process, ensuring that not just workflows, but the data itself is reliable.
  • Configurable and Modular: Dagster pipelines are built with reusable solids, ensuring that common tasks can be modularized and reused across workflows.
  • Intuitive Developer Environment: With a local development environment, users can test and iterate on their pipelines before deploying, reducing the chances of production failures.

Data Catalog: Managing and Organizing Your Data Assets

With the proliferation of data sources, tools, and teams working with data, keeping track of all your assets can be challenging. A data catalog serves as a central repository for managing and organizing all your data assets—from databases to reports.

Data catalog tools don’t just organize data; they form the backbone of data governance. By creating a central repository where data assets are indexed, described, and easily discoverable, these tools ensure that data users—be it analysts, data scientists, or business users—can find the right data, understand its context, and trust its quality. Data catalog tools encompass the following areas:

  • Data Discovery: Discovering what data exists in an organization is often a challenge in itself. Data catalogs allow for comprehensive search capabilities, making it easy to find specific datasets or reports.
  • Data Governance: Data catalogs provide a framework for managing data assets and ensuring compliance with regulations, such as GDPR or CCPA. With features like access controls and data lineage tracking, organizations can ensure their data is secure and auditable.
  • Metadata: At the heart of a data catalog is the concept of metadata—data about data. This can range from basic information like file names and creation dates to more intricate details about data lineage, quality metrics, or business definitions.
  • Documentation: While metadata might tell you what a particular dataset is, documentation delves into the ‘why’ and ‘how.’ It offers insights into the purpose of a dataset, the methods of its creation, and even potential use cases. Together, metadata and documentation ensure that data users can easily locate, understand, and harness data assets.

Alation: As one of the pioneering data catalog solutions, Alation has redefined how businesses perceive and interact with their data. Key highlights include:

  • AI-Powered Search: Alation’s intelligent search engine understands user intent, making it simpler to find relevant datasets even with vague queries.
    Collaborative Annotations: Users can add comments, questions, or insights directly to datasets, fostering a collaborative data community.
  • Integrated Data Lineage: Alation automatically maps out data lineage, allowing users to trace back the origins of data, ensuring trust and reliability.

Atlan: Atlan presents a fresh take on data cataloging with a focus on the modern data team. Its distinct features encompass:

  • Visual Lineage: Beyond just showcasing data origins, Atlan offers a graphical representation, making it easier to understand complex data relationships.
  • Contextual Documentation: Atlan ensures documentation isn’t siloed but contextually integrated with datasets, making it intuitive for users to get a holistic understanding.
  • Integrations: Recognizing the diversity of modern data stacks, Atlan seamlessly integrates with a multitude of tools from the data ecosystem.

Why Each Tool Category Matters?

The digital transformation wave has ushered businesses into an era defined by data. However, as we’ve navigated the various facets of the modern data stack, one might wonder: Why do we need such a diversified toolkit? Let’s delve into the significance of each category, complemented by real-world illustrations.

Data Collection & Pipelining

In the digital realm, businesses interface with a myriad of touchpoints — from mobile apps to e-commerce platforms. Data collection and pipelining tools ensure that data from these myriad sources is aggregated systematically, creating a unified repository for analysis.

Real-World Impact: Consider an e-commerce enterprise. Data pipelines can collate information from website interactions, payment gateways, and customer reviews. This integrated view enables the business to discern patterns, from purchasing habits to payment preferences, driving strategic decisions.

Data Storage

This isn’t just about having a place to dump data; it’s about accessibility, scalability, and security. With robust data storage solutions, businesses can handle petabytes of data, scale as they grow, and ensure that their data assets are protected.

Real-World Impact: Streaming platforms, like Netflix, rely on advanced data storage solutions. This allows them to store vast libraries of content, user interaction data, and more, enabling personalized recommendations for millions of users globally.

Data Transformation and Modeling:

Raw data, in its native form, often isn’t ready for analysis. Transformation tools process this data, ensuring it’s in the right format, free from inconsistencies, and modeled to reflect business-relevant metrics.

Real-World Impact: A global retail chain might pool data from various countries. However, date formats, currency, and other local nuances can differ. Transformation tools ensure that all this data adheres to a standardized format, making global analytics feasible.

Identity Resolution & Customer Profiles

Today’s consumers interact with brands across devices and platforms. These tools create a unified view of customers, stitching together interactions ensuring businesses see them as singular entities, not fragmented datapoints.

Real-World Impact: A user might browse products on a mobile app but finalize the purchase on a desktop. Identity resolution helps businesses link these journeys, offering insights into multi-device purchasing pathways.

Analytics & BI

Data, no matter how well-curated, is meaningless without interpretation. Analytics and BI tools translate data into actionable insights, from visual dashboards to predictive models.

Real-World Impact: A health-tech firm might use these tools to track the spread of a disease, predict outbreaks, and allocate resources proactively, potentially saving thousands of lives.

Data Orchestration

The modern data landscape involves intricate workflows. Orchestration tools automate these workflows, ensuring that data moves seamlessly from one stage to another, processes are streamlined, and resources are optimized.

Real-World Impact: In finance, data orchestration can automate the flow of data from stock markets to analytics platforms, to visualization tools, ensuring traders get real-time insights to make swift decisions.

Data Catalog

As organizations grow, their data assets become vast and varied. Catalog tools ensure that users can find the right data assets, understand their context, and trust their origins.

Real-World Impact: Scientists can use catalogs to find existing datasets that resonate with their study, preventing redundant efforts and expediting discoveries. Data catalogs also play a crucial role in compliance, empowering businesses to prove the integrity of data used in regulatory reporting.

Rivery in the Modern Data Stack: Bridging Gaps and Orchestrating Harmony

The modern data stack is made up of various tools and technologies, each with its own unique purpose and function. These tools work together to create a cohesive ecosystem that enables businesses to effectively and efficiently manage their data assets. Rivery, is a powerful integration and orchestration platform that serves as the bridge between different components of the data tech stack:

  • Holistic Data Integration: While tools like Twilio Segment focus on event data collection, Rivery’s vast connector library facilitates a more holistic data integration approach. Whether you’re integrating marketing metrics from platforms like Facebook and Google Analytics or financial data from systems like NetSuite, Rivery has you covered.
  • Empowering Data Warehouses: Managed cloud data warehouses, such as BigQuery and Snowflake, are revolutionizing how businesses store data. Rivery seamlessly funnels data into these warehouses, ensuring they are continually updated with fresh, actionable data.
  • Enhanced Data Modeling: While dbt is a powerhouse for data transformation and modeling, Rivery ensures that the raw data feeding into dbt is always of the highest quality and consistency. By streamlining the ETL process, Rivery ensures that dbt’s transformation capabilities are used to their maximum potential.
  • Boosting BI and Analytics: Rivery acts as the perfect precursor to BI tools like Looker and Hex. By ensuring data is integrated, cleaned, and orchestrated efficiently, Rivery ensures that BI tools have a robust dataset to derive insights from.
  • Data Governance and Cataloging: Before tools like Alation and Atlan can catalog data, that data needs to be integrated and processed. Rivery plays a pivotal role here, ensuring that data cataloging tools have a rich, comprehensive, and updated dataset to work with.

Best Practices for Building a Modern Data Stack

  1. Select Tools Based on Needs: Not every organization will need every tool. It’s essential to assess the unique requirements of your business.
  2. Optimize for Scalability: Always choose tools that can scale with the growth of your data and business needs.
  3. Prioritize Data Governance: Ensure data quality, integrity, and security by adopting robust data governance practices. This will prevent data silos, inconsistencies, and potential breaches.
  4. Encourage Collaboration: Data is a team effort; ensure tools allow for collaboration across teams and departments to maximize insights and efficiency.
  5. Stay Updated: The data tech landscape is constantly evolving, with new tools emerging regularly. Stay informed about emerging technologies and reassess your data stack as needed.

The Bottom Line

A Modern Data Stack, with the right set of tools, can revolutionize how businesses collect, process, and analyze data. Rivery, with its robust data integration and orchestration capabilities, sits at the command center of this stack, ensuring that businesses can unlock the true potential of their data.

Minimize the firefighting.
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