Raw data is gold, however, transforming it into meaningful insights is challenging without the right tools, and relying on legacy tools is counterintuitive. Today’s data-driven businesses demand a modern data stack – a nimble, cloud-powered arsenal built for speed, flexibility, and insight.
However, choosing the right modern data stack tools can be daunting as an overpowering choice between the last-generation solutions and hyper-specialized services to automate data orchestration.
Choosing legacy solutions comes with the burden of on-premises limitations, manual processes, and hidden costs, potentially stifling the agility, transparency, and scalability of your stack. On the other hand, choosing modern data solutions can add transformative value to your data stack. With their transparent pricing models, effortless scaling, automated operations, and enhanced visibility of processes, modern data stack tools ensure your data delivers insights with agility, efficiency, and clarity.
Here, we discuss modern data stack tools, how they power your data journey from collection to clear-cut insights, and how tools like Astrato, power modern data analytics.
What are the Processes Involved in a Modern Data Stack?
For organizations struggling to translate data into actionable decisions, the modern data stack (MDS) offers “modern solutions”. It’s an integrated ecosystem of mostly cloud-based tools, designed to break down data silos, streamline workflows, and unlock transformative insights. From collection to analysis, each tool in the modern data stack is meticulously designed for a specific stage in the data journey.
These stages correspond to the core processes involved in a modern data stack, which include:
Data Collection: At this stage, raw data is collected from various sources, including databases, data stores, business tools, or social media, to enable holistic analysis and strategic planning.
Data Processing: The raw data that fuels the modern data stack often arrives riddled with inaccuracies, inconsistencies, and missing information. To transform this raw data into a usable format, the data stack passes it through a processing stage through data cleaning to ensure data quality and reliability.
Data Storage: In the modern data stack, processed data is loaded into a cloud data warehouse or data lake, creating a centralized hub for rapid access and retrieval. The purpose of centralized storage is to eliminate scattered data silos, bringing all relevant information under one roof to streamline workflows and foster collaboration.
Data Transformation & Modeling: The transformation process involves statistically analyzing, cleansing, and filtering the data to reveal its hidden structure and relationships. The modeling process then harnesses these data relationships to build sophisticated data models highlighting trends and predicting valuable outcomes.
Data Observability: Data observability is continuously monitoring the pipeline health and performance, identifying bottlenecks, anomalies, etc., before they disrupt data flow and impact your insights.
Data Orchestration: Data orchestration is the process of scheduling the various tasks and processes involved in moving data through the pipeline from source to destination. It involves automating complex workflows to ensure the data gets processed, transformed, and delivered to the right place at the right time.
Analytics & BI: The data journey through the modern data stack ends at analytics and business intelligence (BI). This is where complex data from the pipeline is turned into clear and concise stories, i.e., dashboards, reports, and visualizations that inform decision-making.
Categories of Modern Data Stack Tools
The modern data stack is categorized by its variety of tools and how they are assembled to function harmoniously. These specialized tools offer unique skills to power different stages of data’s journey to insights. ℹ To cover all parts of Machine learning, AI and data, please see Matt Turck’s MAD lanscape.
Here are the key tools that make up the modern data stack:
1. Data Collection Tools
The modern data stack tools for data collection are responsible for gathering information like user behavior, clicks, etc., from various sources like websites, apps, and other digital touchpoints.
Key Features:
Capturing data from multiple channels
Event tracking
SDK generation and integration
Data quality control
Data consent management
API integrations to connect with third-party services
Popular MDS Data Collection and Tracking Tools: Segment & Rudderstack
2. Data Integration/ETL/ELT Tools
Data integration tools automate the process of extracting, transforming, and loading (ETL) data, connecting disparate data sources and streamlining data flow through the modern data stack.
Key Features:
Automated data pipelines and plug-and-play connectors to popular data sources and destinations
Pipeline performance monitoring and logging
Scheduling data transfers
Data transformation
Error handling
Security features for data in transit and at rest
3. Data Transformation Tools
Data transformation tools take raw data from different sources to cleanse and normalize into a consistent format for analysis and modeling.
Key Features:
Code-based and visual data transformations
Data quality checks
Generating and maintaining documentation
Transformation testing
Data integration with other stack tools
Version control
4. Data Storage Tools
Data storage tools like data warehouses and data lakes, handle the critical task of storing and managing all types of data, ensuring its availability, scalability, and security.
Key Features:
Cloud-based data storage and management
Data warehouse/lake integration
Real-time/batch data ingestion
Auto-scaling to handle growing data volume
Data security and access control
Popular MDS Data Storage Tools: Snowflake, BigQuery, & DataBricks
5. Data Orchestration Tools
Data orchestration tools are the coordinators of the modern data stack that automate the execution of various data pipeline tasks, from data extraction to transformation and loading.
Key Features:
Automated task scheduling and execution
Managing task dependencies
End-to-end pipeline workflow management
Pipeline health monitoring
Troubleshooting for errors and managing task reruns, backfills, and ad-hoc runs
Popular MDS Data Orchestration Tools: Airflow
6. Monitoring & Observability Tools
Monitoring and observability tools provide continuous tracking of pipeline performance, data flow, latency, and resource utilization, identifying the root cause of issues and enabling proactive resolution.
Key Features:
Real-time monitoring
Dashboards and visualizations for a clear view of pipeline health
Proactive anomaly detection
Alerting and notification system
Root-cause analysis to pick the problem at its source
Historical data analysis to identify data trends
Popular MDS Data Monitoring & Observability Tools: DataDog
7. Modern BI Tools
Modern Business Intelligence (BI) tools have replaced traditional complexity with self-service accessibility and cloud-powered insights. They are low-code/no-code, easy-to-use BI tools that democratize data exploration by empowering users of all technical skill levels with intuitive interfaces.
Key Features:
Enterprise data analytics, drag-and-drop interfaces, natural language search, and AI-powered data recommendations for intuitive BI
Low-code/No-code visualizations
Built-in collaboration and storytelling features
Built-in metrics layer to centralize metrics definition
Continuous updates
Seamless integration with other modern data stack tools
8. Legacy BI Tools
Legacy BI tools offer powerful data analysis capabilities. While modern BI tools prioritize cloud integration, self-service exploration, and low-code approaches, legacy tools focus on technical expertise, complex modeling, and on-premises deployments, making them less accessible and flexible in the modern cloud-driven data landscape. However, legacy tools lack pushdown SQL, forcing data movement between cloud data warehouses and the tool, hindering query performance and scalability, especially for large datasets. Whereas, modern BI tools often offer built-in pushdown capabilities, allowing for efficient analysis directly within cloud data warehouses.
Key Features:
Wide range of visualization options
Data filtering, drill-down, blending, and statistical analysis capabilities
Complex data modeling
Dashboards for key metrics tracking
Report generation
9. Data Governance Tools
Data governance tools are responsible for upholding crucial regulations and driving responsible data practices in the modern data stack. They enforce and maintain data reliability, quality, security, and responsible use throughout its lifecycle.
Key Features:
Policy management and compliance monitoring
Data cataloging and inventory management
Data lineage tracking
User access management
Automated data retention, archiving, and deletion based on defined policies
Data quality management
Popular MDS Data Governance Tools: atlan & informatica
How to Differentiate Between the Various Modern Data Stack Tools?
The modern data stack boasts diverse tools, each playing a crucial role in generating valuable business insights. The key to building a cohesive data infrastructure is understanding the specific characteristics of each tool category.
To help you navigate these complexities, here is a list of key factors:
Functionality Scope:
The functionality scope of modern data stack tools delineates the specific tasks and processes they are designed to execute within the data pipeline. For example, data collection tools focus on gathering data from diverse sources like web analytics, social media, and APIs, providing the raw material for insights.
Whereas data transformation and integration tools share a common functionality: refining raw data into usable formats for analysis.
Integration Flexibility:
Integration flexibility refers to the ability of a data stack tool to seamlessly connect and interact with various systems, databases, and platforms within the stack.
While robust integration is a primary feature of data integration tools, it is also prioritized by other tools in the stack. For example, some data storage tools also offer built-in connectors to facilitate data migration.
Automation and Workflow Management:
Automation and workflow management revolve around a tool’s ability to take control of the data pipelines, automating tasks and scheduling processes. This is where data orchestration tools shine.
Further, data monitoring and observability tools offer real-time insights, allowing you to manage workflows by maintaining optimal pipeline performance.
Real-time Capabilities:
Real-time capabilities define a tool’s ability to handle data streams and processes instantaneously. This is a key feature of data collection tools that excel at real-time data capture and immediate processing, providing live insights for fast decision-making.
Another category of tools with real-time capabilities are modern BI tools. While not always dealing with live data, BI tools offer near-real-time insights through data visualization and querying.
Scalability and Performance:
Scalability and performance determine how well a tool handles growing data volumes without compromising end results. This is why data storage tools offer an auto-scaling feature to ensure efficient storage and retrieval from massive datasets.
Scaling capabilities also apply to data transformation and integration tools, enabling efficient data processing and fast pipeline execution.
Customization and Configuration:
Customization and configuration determine how much you can tailor a tool to match your specific data needs, workflows, and preferences. Modern BI tools offer extensive customization for visualizations, dashboards, and reporting, allowing users to create personalized views of their data and highlight insights relevant to their roles.
Customizations are also a part of data collection tools that often offer customizable event tracking, data point selection, and filtering options, enabling you to capture precisely the information you need and avoid irrelevant noise.
Security and Compliance Features:
Security and compliance features focus on protecting your sensitive data from unauthorized access, breaches, and non-compliance with regulations. By enforcing policies and ensuring compliance, data governance tools guide responsible data usage and minimize legal risks.
Additionally, data storage tools also prioritize data protection to safeguard your organizational data present in a centralized repository against intrusion and misuse.
Monitoring and Reporting:
Data monitoring and observability tools continuously monitor and report on data pipelines and analysis. They also generate anomaly alerts and detailed reports on performance metrics, ensuring you stay informed and proactive in addressing issues. Moreover, BI tools report historical data to show trends, patterns, and insights to facilitate informed decisions based on past performance.
Also monitoring can also be a secondary feature in data orchestration and integration tools.
Ease of Use/Usability:
Usability focuses on how user-friendly a tool is, determining how easily and efficiently anyone can interact with it to access and analyze data.
Simple interfaces, interactive dashboards, plug-and-play features, drag-and-drop functionality, etc., make data-driven decisions a reality for business users from all technical backgrounds.
However, some tools in modern data stack may offer dedicated interfaces for specific technical roles, while others might require coding or advanced configuration.
Cost and ROI:
Tool costs vary, but return on investment (ROI) depends on their specific value within your data stack. Based on your requirements and budget, the data stack should offer an ideal balance of functionality, cost, and ROI to achieve optimal performance and insights.
Improve Your Modern Data Stack With Astrato
Where Does Astrato Fit in The Modern Data Stack?
Astrato is a no-code business intelligence and data analytics platform that enables everyone to make live data decisions. As a modern data analytics and BI tool, it acts as a flexible data layer in your modern data stack. Its no-code-pro-code approach lets you tailor your data journey, complementing existing BI tools in your stack or serving as a foundation for a new data stack.
Astrato is live-query, meaning that SQL is run directly on the warehouse, enabling enhanced security and leveraging existing ML capabilities of the cloud data warehouse.
Astrato’s cloud-native agility means seamless integration with all big data warehouses – Snowflake, Google BigQuery, Dremio, you name it. Even legacy business intelligence platforms are welcome on board.
With intuitive templates and tools, you can use Astrato to share data insights and build dashboards within minutes.
Unique Capabilities of Astrato as a Modern Data Stack Tool
Sure, other tools in your data stack gather and process data. But Astrato adds a unique spark, igniting insights you never knew existed. Here’s what sets it apart:
Quick & Intuitive Data Exploration: Astrato’s intuitive no-code-pro-code interface empowers everyone, from data analysts and scientists to business users, to explore and analyze data with confidence. Anyone can use it to build fully interactive data apps with drag-and-drop ease.
Unify Legacy and Cloud Data: Astrato bridges the gap between legacy systems and modern cloud data warehouses, seamlessly stitching together all your data sources for a holistic view.
AI-Powered Data Wrangling: Astrato’s smart data suggestions and automation features maintain charts and dashboards, freeing your BI team to focus on uncovering hidden trends and crafting compelling narratives.
Live Data Analytics: Astrato’s live data capabilities deliver instant insights, empowering you to make informed decisions based on the latest information.
Cost-efficiency: Astrato leverages existing data warehouses for computing and storage while also inheriting their robust security measures, saving your resources.
Ready to experience the Astrato difference? Don’t settle for ordinary data tools. Take your modern data stack to the next level, and get started today!
Author credits.
Haziqa Sajid LinkedIn