A modern data stack uses innovative, cloud-based technology to extract, load, and transform data.
Data science is changing faster than we can keep up with it. The traditional data stack was capable of meeting demands in terms of collecting, processing, and transforming data, but modern data challenges require modern solutions.
If you are a data scientist, data engineer, or business intelligence analyst, you can’t afford to rely on the old approach to building a data stack and expect to achieve data analytics efficiency.
In this article, we’ll explore modern data stacks, including components, benefits, and steps to take when building your data stack.
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What Is a Data Stack?
A data stack is a combination of technologies or tools used to compile, clean, store, and transform data.
These tools enable data engineers and analysts to extract and compile data in a single place, transform raw data into something of value, store it, and analyze it as needed.
Data itself is useless unless it’s processed, polished, and analyzed, which is exactly what a data stack strives to achieve.
What Is a Modern Data Stack?
The terms “data stack” and “modern data stack” are often used as synonyms. The only difference is that a modern data stack uses innovative, modern solutions, or cloud-based data warehouses.
Modern data stacks are stored in the cloud and are more accessible and scalable than legacy data stacks. They help address the modern challenges in data management that traditional data stacks failed to solve.
Before cloud solutions were developed, data processing and management looked a bit different. The standard data stack structure relied on the extract, transform, and load (ETL) process. In other words, it meant extracting data from a source, transforming it to prepare it for storage, and then loading it into the database.
A modern data analytics stack, on the other hand, reverses the steps within the process to extract, load, and transform (ELT).
This approach allows businesses to load data into warehouses without first transforming it first.
The ELT approach has many advantages over the old ETL approach because it saves time, increases data usability, improves analytics, and is more cost-efficient.
Key Components of a Data Stack
A modern data stack consists of several components that help streamline data processing and management.
The key components of a data stack are:
The first and most important component is the data source, which is where you pull your data from. This is often a third-party tool used to manage operations, such as a customer relationships management (CRM) platform—for example, Monday.com.
A business typically uses more than one data source and several tools that generate data.
Data ingestion is the process of moving data from the source to storage or a data warehouse. A data warehouse allows you to store data from various sources, and it also manages it.
Ingestion or moving data from sources to your warehouse is easier today, thanks to an abundance of data collection tools.
This is the part of the process where raw data is converted into user-friendly models by changing the data’s format and structure. Data transformation prepares the accumulated data for analytics and makes it more readable.
It helps data analysts, engineers, and anyone else within the organization explore compiled data.
This is the stage where you start to gather insights from the aggregated data to help you make informed business decisions. Data visualization turns raw data into graphic formats and reports to present useful information, such as user behavior patterns and trends.
This goal is achieved through a range of available data visualization platforms, such as Google Charts.
The last component of a modern data stack is reverse ETL, which is also sometimes referred to as operationalization.
This process moves your data from a data warehouse to third-party tools to make it operational. These tools can be a Software as a Service (SaaS) tool or any other system used for operationalization. Some of the tools used for this process are advertising platforms, customer relationship platforms, or sales systems.
Benefits of a Modern Data Stack
Modern data analytics require modern data stacks with cloud solutions that support efficient data integration and meet your data demands.
The benefits of adopting modern data stacks include:
Modern data stacks are faster to set up than the old approach, and they also perform the same work much faster, thanks to advanced cloud-based tools. Legacy data stacks are limited when it comes to processing data, and what previously took hours or days to complete can be done within minutes with modern data stacks.
Cloud-based solutions are far more cost-efficient compared to on-premise solutions because you don’t need hardware or platform maintenance, which can incur additional costs. With cloud solutions, you only pay for what you use.
Another cost-related benefit of modern data stacks is that you can get more work done with fewer employees and resources, so building your own data team is more affordable.
In addition, many modern tools offer free trials, so you can experiment with different solutions until you find the ones that best suit your data needs.
Automation has enabled self-service analytics to expand, meaning that it takes fewer people and less time to manage data. Automation significantly reduces data users’ burden by simplifying and speeding data analysis and visualization.
Ease of Use
The tools in the modern data stack are typically easy to use, so extensive training sessions aren’t required. You don’t need to be extremely tech-savvy to comprehend how these tools work, and there’s no need to install equipment or perform maintenance.
Modern data stacks are designed to streamline data workflows and enable anyone to be a data scientist with adequate tools.
How to Build a Modern Data Stack
Data is an incredibly valuable asset that you can leverage to predict trends and make critical decisions. Investing in data or building a data stack doesn’t require as many resources and effort as it used to, thanks to modern, affordable solutions.
Building a data stack requires finding the right tools for every stage that data goes through, including extraction, storage, and operationalization.
To build a data stack, you need to:
Find a Data Warehouse
The first building component of your data stack is a data warehouse, where you’ll store your collected data and prepare it for operationalization. Data warehouses can support analytics and help prepare the data for the next stages.
Cloud-based solutions are at the core of a modern data stack. They are easily scalable and can store all your data, regardless of the amount.
Implement a Data Ingestion Tool
As mentioned above, data ingestion is the process of moving data from its sources to storage. There is no shortage of ingestion tools, which allow you to transport data, but their features differ. Choose one that aligns with your business needs.
Define Your Modeling Process
Data modeling is the process of taking raw data and preparing it for analytics, whether by creating reports or visual presentations.
While you can build a data modeling process internally if you have the resources and the knowledge of structured query language (SQL), it’s far more convenient to outsource this step to a third-party solution such as Meltano.
Meltano is built to integrate easily with your existing environments, so you can quickly add ELT to your data stack and start extracting and transforming data. The solution is also optimized for easy management, with no training required.
Define Your Analytics Process
The first step in defining your process is to define the goals you want to achieve with data analytics, then extract the data that is relevant for achieving those goals.
This stage of building a data stack also requires the right tools. Meltano’s open-source platform can help you create data models and extract insights. Meltano supports a data build tool (DBT), enabling easy communication between EL and T.
DBT connects to your data warehouse and runs transformation queries. All it takes is to write statements in SQL that transform raw data into data that’s ready for analytic use.
Meltano comes with built-in support for running DBT models, which makes it very convenient to use.
Adopt a Reverse ETL Solution
We’re at the last and crucial part of a data pipeline: reverse ETL or ELT. This is the process of copying data from a data warehouse to operationalization tools you use, such as CRM or advertising platforms.
As mentioned above, ETL has reversed the steps to load raw data first and transform later as needed. This approach speeds up the process of operationalizing data.
Building an ELT pipeline on your own takes time and money, but automated, cloud-based solutions require little upfront cost and virtually no maintenance. Meltano’s ELT and DataOps platform is self-hosted, supports extracting and loading data, and enables easy collaboration.
Another benefit of Meltano is that it can be deployed anywhere and allows you to oversee and control everything, from end to end.
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Modern Data Stack: Key Takeaways
Modern data stacks bring innovative solutions for tackling data collection, management, and use. Adopting this modern solution brings numerous benefits, such as:
- Cost efficiency
- Ease of use
- Wide adoption
When building your modern data stack, the first and foremost priority is to adjust it to your organization’s needs and demands. You need to:
- Find a data warehouse
- Find the right ingestion tool
- Create data models
- Define your data analytics process
Meltano has all the features and tools required for a seamless data experience. Its command-line interface enables you to create your project quickly and start replicating data right away.
It is also customizable, allowing you to build your ideal data stack to suit your specific data analytics needs.
Meltano provides the largest connector library, with 300-plus Singer taps and targets available. It also features a simple, built-in user interface (UI) to run your data stack with ease.
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