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Modern Data Stack: the Definitive Guide for Data Professionals

Data Engineering
Nov 13, 2023
Modern Data Stack: the Definitive Guide for Data Professionals

If you’re like the average mid-to-large business, you probably deal with large volumes of data from all types of sources. And to make sense out of all of that data and tie it to business outcomes, you’ll need to use different tools. On the one hand, there is the tried and tested legacy tool stack. On the other, a modern data stack, with a world of promises.

In this article, we’re going to show what the modern data stack is and explain it beyond the buzzword it has recently become.

What is a modern data stack?

A modern data stack (MDS) is a collection of tools that a modern business uses to collect, store, transform, clean, and visualize data. 

As businesses today embrace a variety of different frameworks, data sources, data visualization needs and more, it’s becoming a necessity to go from legacy tools that handle everything to a stack of different tools that handle different parts of the data management process.

Here is what makes a data stack modern.

Composable architecture

Let’s take a legacy data management tool such as IBM Db2 as an example. It has a certain number of tools for each part of the data management process, from ingestion to modeling and visualization.

You cannot swap out any of these tools for a different one, which is the basis of composable architecture. For example, you want to try out different data transformation tools - but instead, you’ll have to change your entire legacy tool.

monolithic vs. composable architecture
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This is where modern data stacks shine because each tool is easily replaced with a comparable one that does the same process but with better pricing, automation and machine learning features, etc.

Legacy tools are heavy in infrastructure and typically work on-premise. This means that their pricing is inflated, they are difficult to maintain and you can’t swap out different parts of the data management process.

Cloud-first

Modern businesses are increasingly reliant on cloud-based data instead of storing it on-premise. 

Storing data on-premise has a few challenges:

  • It’s expensive
  • It’s difficult to scale and upgrade to a larger data capacity
  • Limited flexibility and compatibility with new tech
  • Increased maintenance in terms of both work and cost
  • Limited accessibility and collaboration
  • Security issues

These are just some of the many reasons why modern BI tools are cloud-native rather than relying on on-premise storage. In many cases, modern cloud-based tools are open-source, which makes it easier for your business to try without huge investments.

on-premise vs. cloud tools
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As an added extra, cloud-first tools have built-in version control. This fosters collaboration and makes it easy to go back and see if issues arose and where it happened.

Ease of use

The average data platform in a legacy system is built for data scientists and engineers. And even for those data professionals, it could take months to grapple with the complex user interface.

Modern tool stacks are comprised of cloud-based tools that are fast to deploy and easy to use. While you still need to have the basic knowledge of e.g. data governance and data integration, you can hit the ground running sooner and start using these tools to guide your decision-making.

Scalability

Imagine that this time next year, you have to handle 10x the amount of data volume that you deal with today. With a modern data stack, this is not a hurdle, as these tools are built with scalability in mind. 

You can work with 50 data sources just as easily as you would with 5. Moreover, creating 50 dashboards is as complex as building one.

Also, if you have a SaaS product, it’s likely that the number of your end-users will increase over time. A modern data stack can scale as your business scales.

The components of a modern data stack

A modern data stack is comprised of different processes and tools that make it easy to collect, store, manage, and visualize data. While your specific use cases may differ, here is the most typical setup for a modern data stack.

The data sources

The data sources are just that - places where your data originates. Modern data stacks can connect to a variety of data sources, thanks to pre-built connectors and APIs. Since contemporary business intelligence tools are cloud-based, that means they easily connect to a variety of cloud based sources.

Some of them include:

  • Databases (Snowflake, Amazon Redshift, MySQL, PostgreSQL, Databricks)
  • Your own API (your SaaS product, most typically)
  • Cloud-based tools (Google Analytics, Salesforce, Hubspot and many others)

The first step in any data process is collecting the data from the sources. Legacy systems have limitations at this step, such as:

  • Limited scalability (can struggle with larger volumes of data)
  • Limited number of sources (typically only connect to Oracle, MySQL, or SQL Server)
  • Batch processing (the data is processed in batches rather than in real-time)

These are just some of the many reasons to opt for a modern data stack right from the very start of your data management journey.

ETL/Data transformation

Before you get to data analysis and visualization, you need to work on the data quality. Coming straight from the data sources, the data will be unstructured, in various shapes and formats, with missing and duplicate fields and more. If you’re looking to build your business on modern data pipelines, this won’t cut it.

The ETL (Extract, Transform, Load) tool takes care of that: it extracts, transforms and loads the data to its destination. Once the process is done, you’re left with clean, uniform data that can later be used for storing and visualization.

the etl process explained
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In recent years, many businesses have preferred ELT (extract, load, transform) to to ETL tools. There are more significant differences than the order of operations. With ELT, the data is transformed at the destination (such as a data warehouse), the process is faster as transformation and loading happen in real time and since it’s cloud-based, it’s also more scalable than the ETL ecosystem.

Some of the best ETL tools include:

  • Fivetran
  • Panoply
  • Azure Data Factory
  • AWS Glue
  • dbt
  • And many others

Cloud data warehouse

Once your data is extracted, loaded and transformed, you’ll need to find a place to store it. First off, no data sources can store large volumes of clean data for you. Second, data visualization tools rarely connect directly to the data source - they require a connection to a data warehouse since the data there is clean, structured and ready to use.

The term “data lake” is also commonly used alongside data warehouses. The key difference is that data lakes store data in all shapes and forms: largely unstructured, raw data. In simple terms, data that is not ready for further use.

Some of the best data warehouse tools include:

  • Google Bigquery
  • Snowflake
  • Firebolt
  • Amazon Redshift
  • And many others

When choosing a warehouse for your data storage, it’s best to look at compatibility with data visualization tools first. Some of them only integrate with certain data warehouses, so do your homework before investing money in either tool. Additionally, be sure to select a warehouse that implements data center sustainability practices to ensure your data is safe and secure.

PS. You can also do reverse ETL and send the data from a warehouse back to your SaaS tool.

Data visualization

Data teams can’t do much with pure data, even when it’s clean, structured and organized. To help themselves and their teams make data-driven decisions, the data and various metrics should be visualized. This means going from rows of numbers to graphs, charts, diagrams, scatter plots, histograms, and more.

Legacy systems are usually very limited in their visualization types. Modern tools such as Luzmo allow data scientists to explore dozens of different visualizations, each ideal for a different use case.

Some of the best data visualization tools include:

  • Luzmo (for embedded analytics)
  • Looker Studio (for marketing reporting)
  • Tableau (for exploratory data analysis in more enterprise businesses)
  • And many others

email marketing dashboard

Since data visualization is often the only part of the workflows that end-users and decision-makers will see, it’s crucial to choose something that conveys your data in a clear way. Ideally, the visualization tool should be self-service so that non-tech users can get to insights more quickly.

What are the benefits of using a modern data stack?

You may be wondering if it’s worth going through the painstaking process of choosing a number of different tools for data just to get a modern data stack. Your data engineers would probably say yes - so here are some of the top pros of using a modern against a legacy data stack.

No vendor lock-in

Legacy stacks are usually tied to a single vendor that offers tools for all parts of the data management process. While this can be an advantage, it means that you’re tied to a single vendor for all processes.

For example, you may find a tool that handles data transformation quickly, but lacks good connectors or has very few visualization options. However, you still have to stick with them as they have all the tools in one place.

Having a modern data stack allows business users to choose the best data tools for each part of the job. It’s not only more effective but also cheaper.

Easier to maintain

If you need to make a change in your data, you don’t have to push updates to the entire legacy system. Instead, you can push updates to just a single layer of your data stack.

For example, your data visualization dashboard needs changes. You don’t have to go all the way back to the source data to push an update. Instead, just head to your dashboard tool and refresh it with new data.

Faster innovation

Data requirements change all the time, and innovations can often force you to upgrade. With a modern suite of tools, changing out one of the components is as easy as canceling a subscription. 

For example, if you want to go from an ETL to an ELT tool, you can simply choose a different tool and connect your data sources to it. The old and new data will go to your new ELT setup the moment you change providers.

On the other hand, a more traditional data infrastructure is not as flexible. If one of the components in your workflow is not working properly, you’re forced to change the entire legacy system.

For example, you added Hubspot CRM to your data sources and your legacy tool does not support it. There are a few choices:

  • Have your developer team work on a complex workaround
  • Purchase an expensive third-party integration
  • Switch to another tool altogether

Each of these choices is costly in terms of time and money.

Wrapping up

Having a modern data stack is no longer nice to have - it’s a necessity with a cloud-based data setup. If you want to help your data analysts and make their job easier, save time and money and provide a better experience for your end-users, switching to a modern data stack is a no-brainer.

We can help you with at least one part of the time-consuming process of making the right choice. With Luzmo, you can visualize your data for your product or website in hours - not weeks or months. No background in data science is needed - we’ll help you get started today.

Grab your free trial and build a functional data stack today!

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