Building an efficient data stack that can handle big data is no small feat, whether due to growing data demands or operational costs. A modern data stack solves these problems by automating and streamlining many data tasks, from sourcing to transformation.
In this article, we will detail what a modern data stack is and its architecture and compare it with legacy data systems.
What is a Modern Data Stack?
Moving data infrastructure from on-premise to cloud makes data access easier and storing big data cheaper. This has led to the development and adoption of many cloud-based tools, enabling companies to build their entire data infrastructure on cloud.
The cloud-based tools for gathering, storing, and processing data are called modern data stack. They are low-code, scalable, and robust tools, built on the latest technologies to optimally handle growing data demands.
Looking to supercharge your modern data stack? Hevo’s no-code data pipeline makes it easy. Try Hevo and empower your team to:
- Integrate data from 150+ sources (60+ free sources)
- Utilize drag-and-drop and custom Python scripts for data transformation
- Benefit from a secure, cloud-based system with SOC 2 compliance
Join 2000+ customers, including Thoughtspot, who trust Hevo over Fivetran and Stitch to elevate their modern data stack. Try Hevo today!
Key Components of a Modern Data Stack
A modern data stack is typically built around the following components.
Data Sources
A data source is where all your data originates. An organization generally has various data sources, including both internal and external data sources.
Internal data sources collect data from your organization’s business units or tools, such as CRM software, website analytics data, ERP systems, inventory data, sales data, and more.
External data sources gather data from outside your organization. For example, social media platforms, research reports, third-party APIs, and news and media are all external data sources.
Data Ingestion
Data ingestion pulls all your data from either internal or external sources and puts it in your preferred storage system. This provides a unified view of all your data, and access to it from a single location.
There are two approaches for loading and transforming your data:
- ETL(Extract, Transform, Load): The ETL approach involves collecting data from various sources, transforming it into a standardized format, and loading it into a central location.
- ELT(Extract, Load, Transform): The ELT is the modern approach of collecting data, loading it into the storage system, and then transforming into a structured format.
The ELT process is faster, because it loads data as it is, in its raw format. This approach is ideal for companies dealing with big data. Hevo Data is a modern ELT tool that can extract data from 150+ sources and load it into the destination.
Data Storage
The above ELT tool moves data from various sources to a central location, but you need a storage system to store that data. That’s when data warehouses and data lakes come into the picture.
Data warehouses are commonly used in the ELT approach because they can only store structured data. However, modern ETL solutions leverage data lakes and cloud warehouses, which can accommodate both structured and unstructured data.
Cloud warehouses like Google BigQuery, Snowflake, and Databricks offer powerful analytical capabilities for you to query the data directly within them.
Data Transformation Tools
Data transformation refers to converting raw data into a structured format. We have data transformation tools to automate and streamline this process. These tools reduce the risk of data consistency, accuracy, and manual errors—all while saving a lot on time.
Some common data transformation tasks include:
- Cleaning data to remove inconsistent, inaccurate, and missing values
- Scaling or standardizing data as a part of the data normalization process
- Converting data types into your preferred format
- Applying filters based on certain criteria
Hevo Data is one such tool that offers various ways of transforming data, whether through code or a user-friendly interface. It supports both pre- and post-load transformations, meaning you can transform your data either before or after loading it to your destination, depending on your needs.
Business Intelligence and Analytics Tools
Now, you can send structured data into business intelligence tools (BI) to draw meaningful insights.
Business intelligence tools are all about helping you explore and find trends in your data. With their intuitive drag-and-drop interface, users can easily create custom reports and dashboards for their use case.
Tableau and Looker are the two popular business intelligence tools in the modern data stack.
Data Orchestration Tools
All your data pipelines require close examination to ensure they run smoothly and in the correct sequence. Data orchestration tools handle this by automating and streamlining data workflows, containing data preparation, integration, and transformation tasks.
They use Directed Acyclic Graphs (DAGs) to schedule and monitor the sequence of data workflows. Users set up the execution flow and intervals, and these tools ensure that workflows run as defined, without errors.
Apache Airflow, Dagester, and Apache NiFi are some common data orchestration tools.
Modern Data Stack vs. Legacy Data Stack
| Legacy Data Stack | Modern Data Stack |
| Definition | Traditional and monolithic approach of storing and analyzing data | Modern data stack leverages the recent advancements in technology to efficiently store and manage data |
| Hosted on | Requires lot of hardware like physical servers and data centers as it is hosted on-premise | Modern data stack leverages cloud technology for all the data storage and processing needs |
| Scalability | Scaling legacy systems often involve significant hardware upgrades | Cloud-based infrastructure offers elastic scalability to scale up or down based on demand |
| Time | Many legacy data systems rely on batch processing, which can be time-consuming | These are faster due to real-time processing |
| Flexibility | They have limited flexibility in adopting new data sources, formats, or tools | Modern data stack offers more flexibility to integrate recent technologies and changing demands into the system quickly. |
| Cost | Legacy data architecture comes with significant upfront cost. Maintenance and operational expenses are also higher in this case. | Modern data stack is the most cost-efficient solution with cloud’s pay-as-you-go pricing models, allowing you to pay only for the resources you use. |
Advantages of Modern Data Stack Over Legacy Systems
The modern data stack has a lot more benefits compared to legacy systems. Let’s explore some significant advantages here:
- Cloud-based infrastructure offers dynamic scaling, automatically increasing resources during high demand and scaling down when not in use.
- Modern data stack has less learning curve compared to legacy systems for non-technical users.
- Cloud warehouses provide powerful storage and analytical capabilities.
- Powerful processing engines ensure data consistency and accuracy throughout the transformation stage.
- Modern data tools automate and schedule regular data quality checks.
- These tools often come with intuitive user interfaces and extensive documentation.
- Modern data stack exponentially increased the data processing speed.
How to Build Your Own Modern Data Stack with Hevo?
The modern data stack is built on cloud infrastructure. The first stage of your data stack involves moving data from its source to your cloud storage, whether a data lake or a warehouse.
With Hevo Data, you can move data from multiple sources, like on-premise DB, excel files, and social media platforms, into your cloud storage systems. Hevo provides 150+ built in connectors, allowing you to automate data extraction from multiple sources to your storage system.
Data transformation is another important component of modern data architecture. Hevo Data offers inflight transformations to clean, aggregate, or filter data during load.
However, you can also load the entire data as it is, and then apply detailed transformations on the loaded data. Hevo supports custom Python and SQL scripts for advanced data transformation and manipulation.
Overall, Hevo is an all-in-one data platform that extracts and loads data, offering transformation capabilities like cleaning or formatting during the load process. You can then use dbt (Data Build Tool) integrations to create the databases you need, which are further used to draw valuable insights.
For a comprehensive look at the foundations, explore Understanding Modern Data Architecture to see how it shapes today’s data stack
Conclusion: Future Trends in the Modern Data Stack
The modern data stack empowers companies to use data more efficiently and make better data-driven decisions.
We are already witnessing the rise of modern data stack across the industry. From startups to large enterprises, many have a matured modern data stack set up in their systems. Here is a glimpse of how the future of data infrastructure might look:
- Data mesh and data fabric technologies are gaining traction, focused on domain-oriented ownership and unified data access.
- Zero ETL, the idea is to store, process, transform, and analyze everything within the source system.
- More streamlined data processing solutions for IoT networks of devices.
If you’re still using a legacy data stack, consider switching to the modern data stack to power your data. Check out the Hevo Data free trial to deploy your modern data stack now.
FAQs
1. What is the Modern Data Stack?
A modern data stack (MDS) is a set of specific tools used to ingest, store, and transform data.
2. How does modern data stack differ from traditional data management systems?
A modern data stack leverages cloud technology to host their data infrastructure, while traditional data management systems are hosted on-premise.
3. What are the best tools for building a modern data stack?
The ideal set of tools for modern data stack include Hevo Data for data ingestion, Snowflake or BigQuery for storage, a dbt (Data Build Tool) for transformation, and Tabluea or Looker for visualizations.
4. What are the key benefits of adopting a modern data stack?
Modern data stack solutions are highly scalable, fast, flexible, and cost-efficient.
Srujana is a seasoned technical content writer with over 3 years of experience. She specializes in data integration and analysis and has worked as a data scientist at Target. Using her skills, she develops thoroughly researched content that uncovers insights and offers actionable solutions to help organizations navigate and excel in the complex data landscape.
