The Modern Data Stack: How The Evolution of Data Architecture Led to The Data Intelligence Platform

The modern data stack is designed to address the difficulties with data collection, storage, and analysis as the volume and complexity of data continues to increase. As business success becomes more reliant on data-driven insights and AI, effective and reliable data management is essential.

So, what is a modern data stack and how are they designed to optimize data use? Read on to understand the differences between modern data stacks and their legacy counterparts, the benefits they can bring to businesses in every industry, and the modern data stack tools that are needed to achieve success from your data. To learn how to accelerate your data and AI goals, read our new executive guide here.

What is the meaning of the term modern data stack?

The term 'data stack' refers to a collection of different technologies that process raw data. A modern data stack consists of tools that are used to ingest, organize, store, and transform data.

These tools are essential for turning data from 'inedible data' (data that cannot be worked with) to 'edible data' (data that can be worked with). The faster that data can be accessed, prepared, and analyzed, the faster organizations can use it for decision-making. An effective modern data stack architecture is therefore crucial for any organization hoping to extract value from their data and react to changes more quickly.

A modern data stack has four main functions:

1. Loading

Loading technologies are responsible for moving data from one location to another. For instance, data needs to be ingested into a data pipeline in order for it to be transformed into a usable state and analyzed for valuable insights.

2. Storage

Once data has been ingested via a data pipeline, it needs to be stored somewhere. Data warehouses and data lakes are two commonly used data storage technologies, however, both have certain drawbacks. One difference is that data warehouses are more suited to storing structured data, while data lakes are better for unstructured data.

Data lakehouse platforms provide a modern solution that is better able to deal with many different types of structured and unstructured data.

Cloud-based data storage solutions, rather than on-premise options, are essential to modern data stacks. They provide easy access to data in highly scalable platforms that can easily adapt to changing needs.

3. Transformation

Modern data stacks are also used to transform data. The transformation process turns 'raw' data into 'refined' data that is suitable for analytics use cases. Many organizations will use a data preparation platform for data transformation.

Data transformation can involve converting data from one format, structure, or value system to another. It's an essential process for data analysis and data-driven decision-making.

There is a vast array of data transformations that can be applied to data, including:

• Normalization - adjusting values in a dataset to a common scale
• Data cleaning - correcting or removing inaccurate or duplicate records from a dataset to improve overall data accuracy
• Filtering - removing irrelevant and unnecessary data
• Aggregation - summarizing or grouping data
• Merging - bringing data together from different sources to create a single dataset

4. Analysis

Once data has been collected and prepared, it's ready to be used for analysis. Modern data stack tools can also handle this step.

Data can be analyzed using machine learning (ML) models in order to identify patterns and trends that can be used for planning and decision-making. Alternatively, processed data may be used as the basis for a variety of applications.

Legacy data stacks

The key to understanding the impact and importance of the modern data stack is looking back at legacy data stacks. Previous data stacks paved the way for the tools and technologies used today, but they had significant limitations that modern data stacks are designed to overcome.

Enterprise data warehouses

Enterprise data warehouses (EDW) were arguably the first data stacks to be commonly used by organizations for data management.

They provide a centralized repository for data accumulated from various sources. This makes them useful for organizations that make use of customer relationship management (CRM) platforms, relational databases, or similar systems that build up large amounts of data.

An EDW typically relies on an ETL (extract, transform, load) mechanism to manage data. In these cases, data is checked against a predefined schema (internal database format) when uploaded, which is known as the schema-on-write approach.

While EDWs undoubtedly played a part in streamlining data management and analysis, they've become somewhat obsolete with the increasing shift towards artificial intelligence (AI) and ML use cases.

This is largely because data warehouses struggle to handle the large amounts of data needed for these use cases. As the amount of data stored within them grows, the costs of data warehouses, as well as their inefficiency, also increase.

Data warehouses also can't handle unstructured data, such as audio, video, text documents, or social media posts, all of which have become vital information for many businesses. Additionally, the EDW makeup is usually designed to store task-specific data. This makes them unsuitable for ML and data science tasks, which rely on the availability of all collected data.

Data Lakes

Data lakes were developed partly in response to the inadequacies of data warehouses for AI and ML tasks. These repositories are designed to store huge amounts of raw data in their native formats. Since they are capable of handling structured, unstructured, and semi-structured data, they are more suitable for ML and data science use cases than EDWs.

In contrast to data warehouses, data lakes don't require data transformation before loading, as there isn't any schema for data to fit. The schema is instead verified when data is queried, which is known as the schema-on-read approach.

Because of this, data lakes are typically considered to be more robust and cost-effective than traditional data warehouses. However, this doesn't mean they don't have other limitations.

Business intelligence and reporting can be challenging when using data lakes, as they require additional tools and techniques to support SQL queries.

Because data lakes store large amounts of data in different types and formats, poor data quality, reliability, and integrity are also frequently a problem. The data stored in lakes can easily end up disorganized, leading to further issues with data accuracy, security and governance.

What's different about the modern data stack?

Because data lakes and data warehouses both fall short in key areas, many organizations find themselves using both to avoid their respective weaknesses. For example, a business could use one big data lake to store their unstructured data, and multiple, purpose-built data warehouses for specific use cases.

In most cases, this results in data silos, increased costs and complexity, with the need to keep data consistent between the two systems posing a significant challenge. In order to solve this problem, a more unified solution is required.

Enter the modern data stack. Designed to solve the challenges of legacy data stacks, there are specific components that all modern data stacks should include.

Open, cloud-based storage

Perhaps the most significant difference between modern and legacy data stacks is that the modern data stack is hosted in the cloud. Rather than relying on physical servers, modern data stacks rely on cloud computing, and are often provided under a SaaS (software as a Service) model.

This means that they typically require less technical configuration by the user. Instead, security, maintenance, and updates are provided by modern data stack companies as part of their service. This makes modern data stacks more flexible and efficient than their legacy counterparts.

It also results in the promotion of end-user accessibility, meaning that a larger number of data professionals can access the data, regardless of location.

A cloud-based solution also increases scalability, as it can be quickly adjusted to meet the changing needs of an organization without the costly, lengthy downtime associated with scaling local servers.

Capacity for diverse workloads and huge data transactions

In addition to supporting a wide range of data use cases, modern data stack tools allow organizations to process huge data transactions. They consist of components that leverage distributed computing frameworks - for instance, technologies like Hadoop or Spark allow for data processing across multiple nodes or clusters, which enables parallel processing of large datasets and complex workloads.

This means that organizations can carry out complex data transactions and tackle a wider variety of workloads, without placing additional strain on their infrastructure. The use of multiple nodes and clusters also allows for data partitioning. This helps distribute the workload, enabling more efficient querying and processing by dividing the data into smaller, manageable chunks.

Techniques like data compression, columnar storage formats, and indexing are also used in the modern data stack to help optimize storage and processing efficiency. These techniques reduce the physical storage space required for large datasets and speed up the processes of data access and retrieval.

Modern data stacks also use incredibly efficient data pipelines, including optimized Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes. This ensures the smooth movement of data from source to destination, regardless of the volume of data being handled.

Support for business intelligence

Modern data stacks are well-equipped to support a wide range of business intelligence use cases, helping organizations extract as much value as possible from their data. They provide robust tools that facilitate efficient data analysis, data visualization, and data-driven decision-making.

Businesses can also benefit from a wide variety of analytics and reporting tools that can integrate seamlessly with modern data stacks. This allows BI platforms to leverage SQL queries, data visualization tools, dashboards, and reporting functionalities to analyze and present insights from the data. They can even integrate predictive analytics or ML models to enable more sophisticated analyses and decision-making.

Some modern data stack architectures even support real-time data processing and streaming. This enables BI tools to provide up-to-the-minute insights and support real-time decision-making, increasing the agility and responsiveness of the organization using them.

Integrated data governance

Integrated data governance is a major benefit of modern data tech stacks. Various tools, processes, and policies are implemented that work to ensure data quality, compliance, security, and accessibility at every stage of the data lifecycle. They also incorporate sophisticated data cataloging tools that document and manage metadata.

Having information about data sources, structures, lineage, ownership, and usage is vital for maintaining an up-to-date centralized catalog. This makes data more discoverable and easier to understand, while also ensuring transparency and compliance.

When it comes to security, role-based access control (RBAC) mechanisms are used to manage and control which users have access to different datasets. These mechanisms help to enforce the organization's security policies, ensuring that only authorized personnel can access sensitive data.

This is especially important for businesses that operate in industries such as banking and healthcare, which deal with a lot of sensitive data. Modern data stacks can also assist with compliance with data regulations such as GDPR and HIPAA. They have features that assist in compliance by enabling data encryption and auditing as well as ensuring adherence to privacy and security regulations.

Many modern data stacks also incorporate real-time monitoring capabilities that help to identify potential data governance issues. Alerting mechanisms can notify administrators about suspicious activities or deviations from established governance standards.

What are the benefits of using modern data stack tools?

Generally, modern data stack benefits can be organized into the following four categories.

Flexibility

Firstly, a modern data stack provides greater levels of flexibility. The various tools can be used to meet the specific needs of the user, while the services available can be added or removed as needed. This modularity allows stacks to be built layer by layer to create the exact modern data stack that an organization requires.

In addition, compatibility with a variety of data formats, databases, and third-party services ensures interoperability and flexibility when incorporating new technologies. Modern data stacks often leverage open-source tools and frameworks, giving organizations greater flexibility through customization and community-driven enhancements.

They also offer options for both real-time and batch processing, which provides greater flexibility for organizations handling different types of workloads by allowing them to cater their data processing to the varying needs of the business.

Efficiency

Because the modern data stack is a cloud-based solution, the speed at which data can be processed has increased exponentially. Large volumes of data can be processed in minutes, compared to the hours in a legacy data stack.

Modern data stacks often incorporate automated data processing and transformation tools. These tools streamline tasks like data cleansing, normalization, and integration, reducing the need for manual intervention. Not only does this make data processing more efficient, but it also helps to improve data accuracy.

Integration with data visualization and reporting tools speeds up the process of translating data into meaningful insights. Many of these tools also offer the added benefit of intuitive interfaces and customizable dashboards, which allow for faster comprehension of data patterns and trends.

Cost

Maintaining large stores of data can be expensive, but modern data stacks can help reduce operational costs across organizations. Due to being cloud-based, there's no need to invest in hardware or complicated infrastructure. As well as reducing the amount of initial investment required, this also reduces maintenance and operational costs across the data stack's lifetime.

Many SaaS modern data stack companies offer consumption-based pricing, which allows organizations to only pay for the services they use. Services that are offered on a pay-as-you-go model only need to be paid for when they're being actively used, which minimizes costs during periods of low or no usage.

Modern data stacks are also continually optimizing resource utilization by automating data processing, minimizing downtime, and improving the overall efficiency of data pipelines. This all adds up to resources that are utilized more effectively and efficiently, which reduces idle time and its associated costs.

Outside of the stack itself, the advanced analytics and ML capabilities available can help users identify cost-saving opportunities. By forecasting demand, optimizing resources, and proactively identifying cost-saving opportunities, an organization can take full control of their budgets.

A modern data stack can even play a part in minimizing the risk and costs associated with data breaches. By implementing robust data governance measures, the risks of data breaches are reduced, as are instances of regulatory non-compliance. This helps organizations avoid any penalties or legal issues associated with non-compliance, which further contributes to cost savings.

Data culture

Data culture is a term that refers to the behaviors, attitudes, and practices within an organization when it comes to its data. It can encompass everything from the beliefs and habits surrounding data-driven decision-making to the value that is placed on data. It also covers the overall integration of data into an organization's everyday operations and decision processes.

A modern data stack can help revolutionize the data culture within an organization.

To begin with, modern data stack tools are designed with usability in mind. This makes data more accessible to employees of all technical abilities. The flexibility offered even means that users aren't restricted to using a single particular tool, instead being able to choose the tool that best meets their needs and level of expertise.

Meanwhile, the robust data governance measures included in the modern data stack work to build trust in the accuracy of data. By providing reliable, well-governed data, these stacks encourage the use of data in decision-making processes. This creates a culture of trust around an organization's data.

Once this trust is in place, the flexibility and scalability offered can start to shine - offering users ways to experiment with new data sources, analytical tools, and methodologies. This helps to build a culture of innovation and exploration.

Modern data stacks can even help to foster better communication and collaboration among teams. Integrated data catalogs, metadata management, and the collaborative features included all help with this. Every stakeholder can hold a shared understanding of data assets, aligning their efforts towards common data-related business goals.

Successful uses of modern data stacks

Organizations across a wide variety of industries have used modern data stacks to revolutionize their approach to data. Here are just a few examples:

AT&T

AT&T is one of the leading telecommunication providers operating in the US today. In the past, they relied on a traditional, on-premises data architecture. However, as the types and amount of data they handled began to multiply, it soon became apparent that they needed a better solution and thus migrated their legacy architecture to a modern data stack provided by Databricks.

This helped them to achieve the real-time insights and automation necessary to optimize the dispatch of their technicians. Now, they can match the troubleshooting skills of their technicians to the customer issue that needs resolving as well as where that customer is located. These are three data points that simply couldn't be unified with their legacy system.

This new, automated approach to technician dispatch has helped AT&T to provide an excellent customer service experience, while simultaneously lowering operational costs by reducing the number of attempts it takes to solve an issue.

HSBC

HSBC is one of the largest international banks, serving over 39 million customers worldwide. With such a large customer base, scalability limitations were inevitable. Or they were - until HSBC adopted a modern data stack.

Now, they can scale data analytics and ML as needed, giving them the power to spearhead a range of customer-centric use cases, including personalization, recommendations, network science, and fraud detection. And all this at a speed that meets their business needs.

"We've seen major improvements in the speed we have data available for analysis. We have a number of jobs that used to take 6 hours and now take only 6 seconds."

— Alessio Basso, Chief Architect, HSBC

USPS

A modern data stack has empowered USPS to efficiently serve customers across the US. The move to a cloud-based lakehouse architecture allows them to respond swiftly to new data challenges and offers new opportunities for innovation.

What's more, USPS OIG is now better positioned to investigate, audit, and research postal operations and programs. This helps them in their fight against fraud, waste, and abuse, ensuring the efficiency and integrity of the postal service for many years to come.

Embrace a modern data stack architecture with the Databricks Data Intelligence Platform

A modern data stack architecture is the next step in the evolution of the data stack. The scalability, efficiency, and enhanced governance it offers allows businesses in all industries to realize the full value of their data efficiently and securely.

The Databricks Data Intelligence Platform is built on lakehouse architecture which combines the best aspects of data lakes and data warehouses to provide a modern data stack that will help you reduce costs, increase productivity and deliver your data and AI initiatives faster.

Databricks has focused its engineering efforts on incorporating a wide range of industry-leading software and hardware improvements in order to implement the very first data intelligence platform.

Our approach capitalizes on the computing advances of the Apache Spark™ framework and the latest networking, storage, and CPU technologies to provide the performance customers need to simplify their architecture.

These innovations combine to provide a single architecture that can store and process all the data sets within an organization and support a wide range of analytics for vital insights.

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