Over the years, companies have struggled to stack their data correctly, but introducing them to the Cloud, SaaS, and open-source data stacks made a significant change.
It is easy to understand why companies are eager to make radical changes considering the data stack. Since a lot of data comes from different sources, many reports must be written manually. This often results in significant errors, leaving companies without time and money and inaccurate data.
Companies are looking for new stack models because data and computational power requirements increase as the company grows.
Imagine the old data stack models as a large shopping cart. Your cart fills up as you proceed along your journey. At some point, if you need to reach for something you put in the cart when you started shopping, you must pull everything out to get it. With a modern data stack (MDS), it is as if the data is sitting on a shelf.
Source: Unsplash
This transition is most evident in the way the data stack is functioning. The process of old data stacking (ETL) and modern data stacking (ELT) may have similar tasks, but the outcome is quite different.
ETL vs. ELT: What is the difference?
The process of data stacking relies on three steps:
- Extracting data from data systems and external sources
- Transforming the data for storage
- Loading it into the database
Data warehouses are often row-based relational databases, which do not scale well for data analytics workloads due to data being distributed over multiple disks or servers. Even with the usage of additional fast technologies, their maintenance remains largely complicated.
The term ETL (Extract-Transform-Load) originates from the fact that data engineers had to write transformation jobs before loading data into legacy data warehouses due to the limited processing power.
Moving to ELT (Extract-Load-Transform) as a Modern Data Stack, analysts are no longer reliant on engineers to transform data. With MDS, they can provision and load the data within minutes.
How is MDS Beneficial?
Helping Companies Transfer Focus from IT to Business
MDSs free organisations from IT-related worries and allow them to focus on other areas of their operations.
Instead of wasting time on the administration and performance optimisation of the traditional data stack, companies can have smaller data teams that can focus on higher-value data tasks.
Managing data with ETL was usually performed by engineers, since it required more technical background. In an MDS, the tools are designed to be more accessible. Meaning the data professionals are less dependent on data stacking. MDS was designed to be more self-serve, allowing for easier access and data extraction.
Operationalising Business Intelligence and Artificial Intelligence
With the modern data collection process, iteration is much faster, and IT teams no longer need to be a huge part of the process. This helps non-tech companies generate actionable insights in hours instead of weeks and use data from various sources.
Storage Savings and Easier Analytics
Data preparation platforms, such as Trifacta, have made it possible for business users to decide what is acceptable, what needs refining, and when to move on to analysis.
Using MDSs reduces hardware maintenance and its associated costs. As a result, this has revolutionised how organisations conduct analytics.
Data Protection and Regulation
We can all agree that cybersecurity threats have risen in the past decade, causing companies to source different protection measures.
Not securing the stack can cause disastrous consequences for a company.
Keeping data from breaching is hard enough. Relying on ETL for protection without additional effort was an impossible task. In MDS, security tools like Altan are designed to help tremendously in responsible AI, data regulation and privacy.
This means that the tool is programmed to permit access to the information only to authorised personnel and decrease the probability of a breach, but also to respond accordingly in that scenario.
How Does MDS Work?
MDSs contain a variety of components. Each one is equally important and can be modified for different companies.
Data sources
Stack data can originate from different sources. It could be a personal database or a third-party source.
Data intake
Data intake tools such as Weld help companies move and normalise collected data to their storage. The point of this step is to prepare accumulated data for a clean environment.
Data storage
In a modern data stack, this usually takes the form of a data warehouse, which aggregates the data coming from the data sources.
It is essential to upgrade data storage constantly to avoid gaps in features and intelligence. Most warehouse solutions must also include features to perform initial analytics, allowing for more efficient and effective data processing in the later stages.
The transformation and modelling of data
At this stage of data stacking, the tools we mentioned before come in handy. During this process, tools help in data processing, aligning teams around common metrics, and ensuring that data is always communicated in the same language throughout the organization.
Data visualisation
Data visualisation or data analytics is one of the final stages of MDS. All data that has been collected in previous stages are now being turned into actionable content. This means the data will be represented in graphs, charts, tables, and any other format that can be easily understood, often with tools such as Metabase.
Data operationalisation
In the final stage, it’s time to deliver the data to where it will be the most useful. Data operationalisation, also called Reverse ETL, is the process of applying the extracted and processed data to business applications and software.
How To Create Your Own MDS?
Each organization has different working systems, goals and needs. The best way to measure up to those needs is to create a new personal MDS. Here are some variables that factor into this process.
Warehouse
The warehouse is the most crucial part of this journey. It will have a significant impact on the data stack. Currently, Amazon Redshift, Snowflake, and BigQuery are some of the most popular data warehouses.
Each warehouse has different features to consider, including type, price, functionality, and technical specifications. Selecting the right warehouse is crucial to a smooth data stack and requires weighing factors like budget and long-term goals.
Source: Pixabay
Having a large storage unit with no shelves, for example, requires you to place all your collections on the floor and stack them one on top of another. Nevertheless, organising your collections using a shelf can be very helpful in maintaining clear and organised storage, as well as help you locate, use, and re-track them.
Data intake tool
Data intake or data ingestion is a step based on transferring the data from different sources to the warehouse. This way, the intake tools determine how big the database will be.
It is very important to have good intake tools that can ingest the data from various systems and store it in the warehouse. There are many affordable and easily applicable tools like Fivetran or Airbyte that vary in features, pricing models, and levels of support.
Defining the data modelling process
It is important to focus on a narrow set of features in the last components of the data stack after gathering the data warehouse and ingestion stages.
In order to model the data effectively, it is important that a well-thought-out process is developed. Unlike the other stages, this one requires a solid knowledge of SQL. If this step is done correctly, the alignment of common metrics will be much easier.
Analytic process
The purpose of data analytics is to help users explore and discover insights from their data. In addition to developing dashboards and other monitoring tools, visualisations and other representations are usually used in this process. This is one of the least software-dependent processes since the main goal is to find an analysis that can work with the provided data and maximise its value without having to know SQL.
Finding the best Reverse-ELT
This is the final stage in creating your MDS. Reverse-ETL basically means that you are extracting the collected data from the warehouse so you can properly use it. Choosing the right Reverse-ELT can affect the quality of data extraction and, by that, also the quality of data usage to its full potential.
Good and Bad MDS
There is ultimately only one thing that matters: the user experience. If an organization has the best and most expensive tools, that doesn’t guarantee success.
It is essential to create MDS that can provide the best experience for the users and meet all of their needs. Good MDS should include:
Simplicity
A simple MDS is not a bad MDS. Not all of them have all the components but are still functional and within the needs of an organization. Easy access and managing can be a big plus with users, and in time, more components can be added.
Availability
So far, the data stack was time and resource consuming because accessing the data was complicated. MDS should be accessible and inclusive since all the data is stored in a Cloud. This way, the system is available for different departments and users can get more familiar with the system, build trust and find the data in question.
Help in creating an MDS
It is very important to know that each organization should strive to create its own MDS. Implementing someone else’s successful MDS does not mean it will be successful in our organization.
Start-ups and large corporates alike can take advantage of companies that specialise in helping them set up and architect modern data stacks appropriate to their contexts.
How the Modern Data Stack Promotes Data Maturity
There are many companies that make decisions based on their data results and not their intuition. Those companies are data-driven.
Using predictive modelling to understand the needs of its users goes beyond basic analysis. As a result, data-driven companies are 240% more likely to have a competitive advantage since they are using calculations and previous experience to determine future outcomes. The data stack is the only factor that is applicable in decision making.
Creating data maturity means using 100% of the collected data when making company decisions. Using MDS in terms of integration, storage and analysis helps organisations have easy access and leverage all data.
Author bio
Travis Dillard is a business consultant and an organisational psychologist based in Arlington, Texas. Passionate about marketing, social networks, and business in general. In his spare time, he writes a lot about new business strategies and digital marketing for SEO Turnover.