In the age of data-driven decision-making, businesses are increasingly relying on efficient data management solutions. Traditional data infrastructures have often struggled to keep pace with the growing volume and complexity of data. This is where the concept of a "Modern Data Stack" comes into play, offering a fresh perspective on data management.
As per Gartner report, By 2025, 51% of IT spending will have shifted from traditional solutions to the public cloud, compared to 41% in 2022. Almost two-thirds (65.9%) of spending on application software will be directed toward cloud technologies in 2025, up from 57.7% in 2022. The data statistics are going crazy as well. In the year 2022, 97 zettabytes of data generated world wide. Which is expected to be 3 times in the next 3 years. With this sky-high data being generated, organisations need to move quickly to the technology that can handle an extortionate amount of data with scalable workloads.
As of the current MAD 2023 landscape, enterprises have plenty of options to choose which platform/tools they want to modernize their tech stack.
What is a Modern Data Stack?
At its core, a Modern Data Stack refers to a set of tools, technologies, and processes designed to streamline the collection, storage, processing, and analysis of data. Unlike the monolithic and often rigid approaches of the past, modern data stacks embrace flexibility, scalability, and ease of use.
The Cloud Data Warehouse Revolution
The trajectory of data warehousing transformed in 2012 with the advent of Amazon's Redshift. This seminal launch set the stage for a new era, influencing subsequent solutions such as Google BigQuery and Snowflake. This shift stems from the divergence between MPP (Massively Parallel Processing) or OLAP systems, exemplified by Redshift, and OLT systems like PostgreSQL. A deeper dive into this subject awaits on our blog, dedicated to exploring data warehousing technologies.
Comparison with Traditional Approaches
To understand the value of a Modern Data Stack, it's essential to compare it with traditional data management approaches. In the past, data warehouses and ETL (Extract, Transform, Load) processes were at the forefront of data management. These systems were often expensive to set up, time-consuming to maintain, and struggled to accommodate rapid data growth.
In contrast, Modern Data Stacks leverage cloud-based infrastructure, which provides scalability, elasticity, and cost-efficiency. Tools like Airbyte, DataHub, Dagster, Great Expectations and DBT etc. have emerged as key players in this space, offering unique features and capabilities.
To visualise, an advanced Modern Data Stack architecture can include following technologies and tools:
- Integration: Transferring data from one location to another, facilitated by tools such as Airbyte, Fivetran, and Dataddo.
- Warehousing: Centralizing all your processed data into a unified repository, utilizing tools like Amazon Redshift, Snowflake, Google BigQuery, and Databricks.
- Transformation: Transforming your data into actionable insights aligned with your business rules and key performance indicators (KPIs), leveraging tools like dbt, SQL, and Python.
- Workflow orchestration: Unifying data from diverse sources, merging and refining it for analysis, utilizing tools like Airflow and Dagster.
- BI & analytics: Analyzing all the information your organization has collected and making data-driven decisions - with tools like Apache superset, Metabase, Power BI, and Tableau.
- Data catalog & data quality: Centralize the data catalog from different tools used by organization from DataHub and maintain the data quality check regularly with tools like Great Expectations.
An example realization of the Modern Data Stack
Real-World Scenarios
Modern Data Stacks have found a home in various real-world scenarios:
- E-commerce Analytics: Online retailers utilize Modern Data Stacks to collect and analyze customer behavior data in real-time, allowing for personalized recommendations and optimized marketing strategies. Read more about how the customer satisfaction segment use case is implemented with Airbyte.
- Healthcare Data Management: Healthcare providers leverage these stacks to securely manage patient records, ensure compliance with regulations, and gain insights for improving patient care. Read how the digital health care use case is implemented in Vida health with Modern tools.
- Financial Services: In the financial sector, Modern Data Stacks are used to monitor market data, detect fraud, and make informed investment decisions. Austin capital bank transformed its digital banking journey with Modern data tools.
- Self-Service Analytics: Industries are moving from traditional enterprise data warehousing to self-service analytics approach to unlock the power of data, which is not only limited with fewer people in the organisation. Read dbt+Snowflake case study with Oil & Gas company, that helps them to enable self-service analytics.
Benefits of a Modern Data Stack
- Scalability: Modern Data Stacks can scale effortlessly to accommodate growing data volumes, ensuring that businesses are well-prepared for expansion. A major difference between on-premise and cloud workloads are totally based on scalable requirements. Modern Data Stack tools are designed to horizontally scale depending on workload. For instance, Amazon Redshift, a popular cloud data warehouse in modern data stacks, can scale from gigabytes to petabytes of data without compromising performance.
- Cost-Efficiency: Cloud-based infrastructure reduces capital expenditure and offers a pay-as-you-go model, helping organizations manage costs effectively. These modern tools are following the user/usage based costing mechanism to provide transparency in the cost. A costing model comparison between tools can be seen at this blog.
- Flexibility: These stacks support a wide range of data sources and formats, promoting data diversity and accessibility. To increase the adoption within the organization, Modern Data Stack companies started releasing on-premise connectors to integrate seemlessly. Also to acquire new customers, these tools already started integration with legacy tools(ERP Systems, CRM Systems etc.).
- Data Quality: With built-in data governance and quality assurance features, Modern Data Stacks help maintain clean and accurate data. To achieve any organisation revenue target, picking the right audience for a campaign or establishing a correct product-market fit, all of the aspects require correct data to be in place.
Drawbacks and Considerations
While Modern Data Stacks offer numerous advantages, they are not without their challenges:
- Integration Complexity: Integrating various tools within the stack can be complex and may require a skilled data engineering team.
- Skill Gap: Organizations may need to invest in training or hiring data professionals with expertise in these modern tools.
- Security Concerns: Storing data in the cloud raises security and privacy considerations that must be addressed comprehensively.
In conclusion, a Modern Data Stack represents a significant shift in how organizations manage and leverage their data. It empowers businesses to harness the full potential of their data assets while addressing the limitations of traditional approaches. As data continues to play a pivotal role in decision-making, adopting a Modern Data Stack becomes a strategic imperative for staying competitive in today's data-driven landscape.
