Data has been a part of our human identity since the time of the ancient Romans. During the 1880 US statistical data, our growing interconnections caused us to run into scale issues. We have gone from having 0% of the world on the web to having 59.5% of the world on the web. 4.32 billion cell phone users generate a massive data feed. How has the nation dealt with the need to analyze such large amounts of data? To manage this enormous amount of data, we devised punch cards, relational databases, the cloud, Hadoop, and distributed computing; today, we use data engineering to deal with such big datasets.
Data engineering is essentially about developing and constructing the data infrastructure required to collect, clean, and format data to be accessible and helpful to end users. It is sometimes regarded as a subset of software engineering or as a distant cousin of data science. As data handling is becoming more complex, data engineering skills evolve. Today, data transformation is more than just warehousing and ETL (extract, transform, load) functions.
Our initial forecast was that the data would be critical for the board. Of course, there are board meetings that would be impossible to hold without data. But we did blow it on this one by taking an overly aggressive approach. We have yet to see a data role document directly to the board, which is unlikely. A review of data competency for organizations is increasingly happening at the board level, which relates to another variable of oversight for the leadership team.
The following prediction was that each team would have dedicated data engineering support, which proved to be true. Different structures are emerging now. More businesses are recognizing the importance of dedicated data support throughout the organization. We also predicted that more rainbows would solve data problems, which is also apt to some extent.
Data platform commoditization is continuing and will accelerate. The pipelines and ELT are still at the forefront, but we see more and more categories rise. Because the market is still impressionable, this effect will persist. Infrastructure for real-time and streaming will become the norm. Companies are and will continue to invest heavily in real-time infrastructure.
Let us look at the current state of the data space and see some predictions for the future.
Even though data engineering has existed for a while, modern cloud architectures are still in their infancy. The excitement of how far we have come in such a short time can make it easy to neglect that we are still in the early stages of the data revolution.
The emergence of new terminology and the trouble of defining terms that have become prevalent in the last year or two is one indicator of industry health. For example, when you mention CRM, everyone immediately thinks of Salesforce and has a precise vision for its role in the business. It is due to terms popularized by companies pioneering new technologies, which their product marketing teams advertise. As users layer technology and develop use cases, the industry will sooner or later help influence interpretations for specialized tooling and architectural patterns.
The data tools we use today are becoming more and more advanced. Specific data tasks, previously challenging, are now made simple and automated by good tooling. Cloud data warehouses and data lakes are the core aspects, but the variety and number of tools enclosing and supporting them are growing. Choosing a high-quality tool is immensely beneficial for data professionals, but the evaluation process is challenging.
The marketing technology terrain has seen a significant increase in tooling. However, data infrastructure is different, and the risks are many when the implications are stack wide. The agony of coping with increased complexity has ramped up yet another trend, spawning an entirely new class of data tooling based on software engineering principles.
While data engineering is an engineering discipline, the lack of data processes and tooling has always surprised software engineers. Teams and data leaders are rapidly trying to implement software development principles, and various tools are available to support them.
Today, advanced data teams increasingly want APIs from their data tooling that enable them to handle their data stack in a way that resembles software development's CI/CD processes. This trend will continue as data teams avoid the hassle of dealing with an expanding number of distinct user interfaces for stuff like pipeline management.
Data quality and governance are the most complicated to solve of all the challenges posed by an increasingly complex stack. Companies are trying to resolve this at various points along the stack. Some approach the issue at the capture points, others within pipelines, and some others after the data is in the data warehouse. Many millions of dollars of investment is in companies developing these products. But, we are yet to see widespread acceptance of a stack-wide governance architecture that performs well for many businesses.
Surprisingly, warehouse governance mechanisms haven't seen widespread adoption, especially given how critical data stores are to modern businesses. It's possible that post-load governance isn't as crucial for data teams on the ground as it seems. We also doubt if modern tooling for analytics engineers, which also solves management in the specific context of reporting, reduces the urgency of more comprehensive governance.
Companies nowadays are doing amazing things with data. Customers are constructing and updating centralized customer profile stores in low-latency key-value stores, exposing that database to their entire stack via APIs, and then delivering ML-driven customer experiences across their stack. Putting ML to work this way means that "the future" has arrived. The relationship between accessibility and cost and the emergence of new technologies enables these innovative architectures. It is becoming easier to use advanced ML technologies when they are becoming less expensive.
Although machine learning is not new, the required quantities of data, the complexity, and the infrastructure costs have historically been barriers to entry for most businesses. The aftereffects of those barriers persist—even today, many companies use ML for insights and learning rather than driving actual customer experiences.
However, this is rapidly changing. The quantity problem is being solved by the commoditization of data collection, while standardization and deep out-of-the-box integration make the required infrastructure 'turn-key.' One exciting trend in this space is ML-as-a-service, delivered directly to the warehouse, making it easier for businesses of all sizes to build competence in operationalizing ML. ML SQL is another trend that is picking pace. Although ML tooling is still in its infancy, SQL has the potential to democratize ML.
However, the most exciting times for analytics are just ahead.
The advancements in machine learning for the data stack are very promising. But, looking at the broader market, you'll notice that many companies are still working hard to build robust, scalable analytics. Companies are incorporating more data into an ever-increasingly complex stack. Analytics is an ever-changing target. One symptom of this problem is that while localized analytics improve, comprehensive analytics across business functions become more difficult to obtain.
Tools for the modern stack and new analytics architectures are quickly resolving this issue, implying that the future of data analytics is bright. One of the most promising developments is a "metrics layer" architecture, which abstracts analytics from specific, localized tooling and enables teams to manage stack-wide analytics data modeling from a centralized location. This solution is used directly in the warehouse through tools built on query engines, which extend the metrics layer metaphor to operate 'on top of the entire stack regardless of the tools, leveraging MPP for analytics without moving data. Both approaches are likely to be used at the enterprise level.
Reverse-ETL is yet another exciting trend. Reverse-ETL pipelines allow you to send enhanced or simulated data from the warehouse to both pivotal and localized analytics tools, increasing the power and value of those analytics.
The massive movement of companies organizing themselves around the importance of data is at the root of all trends in data stack architecture and tooling. According to DICE's 2020 tech jobs report, data engineering jobs increased by 50% YoY. The big news, however, was the significant salary growth from 2018 to 2020, which is now on an equal level with software engineering. The battle for engineering talent has always been fierce, and data engineering has emerged as a combat force.
The longer-term story, however, is how intelligent data teams organize around the toolset to deliver value and trust across their organizations. Simply having a dedicated data function within the organization will not suffice; the most successful businesses will build modern data teams, not just modern data stacks.
This blog provides a clear picture of the data industry in 2022, first by reassessing 2021 predictions and then outlining the state of data engineering in 2022 with new predictions. Read the blog to learn about the current state of data engineering. Don't miss out on the opportunity to delve into the current hot trend.
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