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Modern data stack (MDS) is a collection of cloud-hosted tools that enable a business to integrate data very effectively. We believe that MDS serves as the foundation for MLOps and DataOps. 

MDS creates clean, dependable, and always accessible data that enables business users to make sef-service discoveries, enabling a truly data-driven culture. 

In this post, we are going to cover the major modern data stack trends and how they are reshaping the role of data engineering. 

Here are the trends we are going to cover: 

1. Data Infrastructure as a Service: The role of data infrastructure engineer was still very much in existence five years ago, even though it was clear that DBAs (database administrators) were going extinct. Clearly, a sizable percentage of infrastructure-related labor is now being managed by specialized multi-tenant elastic services in the cloud.

Snowflake, BigQuery, Firebolt, Databricks, and others offer managed pay-as-you-go cloud data lakes or warehouses. Data infrastructure landscape managed services are starting to arise outside of data warehouses. For Apache Airflow alone, there are three paid options: Astronomer, Amazon’s MWASS, and Google’s Cloud Composer.

Together, these services will be more reliable and economical than any individual data infrastructure team.

2. Data Integration Services: The 2017 data engineer spent a lot of time and felt a lot of pain processing atomic REST APIs to extract data from SAAS silos and onto your warehouse. Due to the popularity and rising quality of services like Fivetran and its open-source rivals Meltano and Airbyte, this is currently a lower priority. Many organizations replaced their questionable scripts with managed services that can quickly sync atomic data to your warehouse, which was a sensible move.

Given these solutions’ availability and price, it would be folly to attempt the from-scratch method. In 2021, working with a data engineering services provider like one of the above choices made perfect sense. 

3. Reverse ETL: The integration of data from data warehouses into operational systems is addressed by reverse ETL, a recent technique (read more about SaaS services here). It seems to be a fresh, modern way of managing some of what we may typically call master data management (MDM). Some of the principles and ideas behind EAI and EII still hold today, so if you’re interested in technology that has been around for a while, you might want to learn more about them. To give an example to individuals unfamiliar with the term, the common use case is to combine behavioral product information into your CRM to perform targeted product-engagement initiatives.

High Touch, Census, and Grouparoo are excellent alternatives that make it quite simple to distribute your entity data and its attributes to a variety of endpoints. When it comes to data integration, the challenges of pulling data from third-party APIs are comparable to the difficulties of syncing data from your warehouse back to a third-party SaaS product. Reverse ETL is gaining popularity, which is good because it was inefficient for each organization to keep developing the same technique and fragile results.

Teams in charge of writing bespoke data integration scripts should generally consider those scripts to be technical debt and deliberately move away from them.

4. ELT > ETL: While employing an ELT-based transformation strategy is nothing new (I have personally preferred utilizing ELT over ETL for the past 20 years), it is growing in popularity. Terms like Informatica, DataStage, Ab Initio, and SSIS are getting harder for most practitioners to understand. It only makes sense to move the computation to the place of the data. It makes perfect sense to construct the datasets using the same incredibly efficient query engines that create distributed cloud databases, which are a miracle of distributed system technology.

Because the justification for utilizing the same engine to perform transformation and analytics queries is so strong, we’ve found that the Spark ecosystem has gradually developed into a database with the rise of SparkSQL. Meaning that, in addition to databases being better at supporting ETL workloads, some ETL systems are becoming better at acting as a database.

5. Analytics Procedure’s Democratization: The democratization of data access has become a tired and clichéd narrative. A more noteworthy development is the increase in accessibility of the entire analytics process—the way in which data is captured, gathered, processed, and used—to more people.

PMs are becoming more familiar with instrumentation, more people are picking up SQL and contributing to the transform layer, and more people are making use of the abstractions that data engineers have made available in the form of compute frameworks. 

Software developers are also becoming more data-savvy, whether they are incorporating analytics into their own products or using them themselves.

6. Computation Frameworks: We are witnessing more abstractions in the transform layer. Additionally made popular by Airbnb’s Minerva, Transform.co, and MetriQL are the metrics layer, feature engineering frameworks more akin to MLops, A/B testing frameworks, and a Cambrian explosion of in-house computation frameworks of all kinds.

This field is starting to take shape, regardless of what you want to call it—data middleware, parametric pipelining, or computational framework.

7. Accessibility: It is becoming simpler for smaller businesses to be powerful with data, building on other trends, particularly the one about “data infrastructure as a service” that was mentioned earlier. This is largely due to the accessibility of pay-as-you-go SAAS solutions. These services often grow infinitely, give great time-to-value, seamlessly interface with one another, offer good compliance assurances (SOC2, HIPAA, GDPR/CCPA, etc.), and are incredibly economical for modest data volumes. Additionally, you can use the top data infrastructure team’s services as a service.

Companies that were created in the “analytics age” have a competitive advantage because they can improve their data skills very early. Accessibility appears to be an important consideration for the current data stack.

Conclusion

In this era, no business can remain competitive without data engineering consulting services. A modern data stack is a technology that can make a business more efficient in terms of time, money and labor. It is more accessible, scalable, and rapid than the traditional data stack. With the help of MDS, a business may become a cutting edge, data-driven company, which is crucial for creating business solutions..