The modern data stack reigns supreme because it supports use cases and unlocks value from data in ways that were previously, if not impossible, then certainly very difficult. Machine learning moved from buzzword to revenue generator. Analytics and experimentation can go deeper to support bigger decisions.
The same will be true for each of the trends below. There will be pros and cons, but what will drive adoption is how they, or the dark horse idea we haven’t yet discovered, unlock new ways to leverage data. Let’s look closer at each.
Zero-ETL
What it is: A misnomer for one thing; the data pipeline still exists.
Today, data is often generated by a service and written into a transactional database. An automatic pipeline is deployed, which not only moves the raw data to the analytical data warehouse but modifies it slightly along the way.
For example, APIs will export data in JSON format, and the ingestion pipeline will need to not only transport the data but apply light transformation to ensure it is in a table format that can be loaded into the data warehouse. Other common light transformations done within the ingestion phase are data formatting and deduplication.
While you can do heavier transformations by hard-coding pipelines in Python, and some have advocated for doing just that to deliver data pre-modeled to the warehouse, most data teams choose not to do so for expediency and visibility/quality reasons.
Zero-ETL changes this ingestion process by having the transactional database do the data cleaning and normalization prior to automatically loading it into the data warehouse. It’s important to note the data is still in a relatively raw state.
At the moment, this tight integration is possible because most zero-ETL architectures require both the transactional database and data warehouse to be from the same cloud provider.
Pros: Reduced latency. No duplicate data storage. One less source for failure.
Cons: Less ability to customize how the data is treated during the ingestion phase. Some vendor lock-in.
Who’s driving it: AWS is the driver behind the buzzword (Aurora to Redshift), but GCP (BigTable to BigQuery) and Snowflake (Unistore) all offer similar capabilities. Snowflake (Secure Data Sharing) and Databricks (Delta Sharing) are also pursuing what they call “no copy data sharing.” This process actually doesn’t involve ETL and instead provides expanded access to the data where it’s stored.
Practicality and value unlock potential: On one hand, with the tech giants behind it and ready-to-go capabilities, zero-ETL seems like it’s only a matter of time. On the other, I’ve observed data teams decoupling rather than more tightly integrating their operational and analytical databases to prevent unexpected schema changes from crashing the entire operation.
This innovation could further lower the visibility and accountability of software engineers toward the data their services produce. Why should they care about the schema when the data is already on its way to the warehouse shortly after the code is committed?
With data streaming and micro-batch approaches seeming to serve most demands for “real-time” data at the moment, I see the primary business driver for this type of innovation as infrastructure simplification. And while that’s nothing to scoff at, the possibility of no copy data sharing to remove obstacles to lengthy security reviews may result in greater adoption in the long run (although, to be clear, it’s not an either/or).
One Big Table and Large Language Models
What it is: Currently, business stakeholders need to express their requirements, metrics, and logic to data professionals, who then translate it all into a SQL query and maybe even a dashboard. That process takes time, even when all the data already exists within the data warehouse. Not to mention on the data team’s list of favorite activities, ad-hoc data requests rank somewhere between a root canal and documentation.
There is a bevy of startups aiming to take the power of large language models like GPT-4 to automate that process by letting consumers “query” the data in their natural language in a slick interface.
This would radically simplify the self-service analytics process and further democratize data, but it will be difficult to solve beyond basic “metric fetching,” given the complexity of data pipelines for more advanced analytics.
But what if that complexity was simplified by stuffing all the raw data into one big table?
That was the idea put forth by Benn Stancil, one of data’s best and forward-thinking writers/founders. No one has imagined the death of the modern data stack more.
As a concept, it’s not that far-fetched. Some data teams already leverage a one-big table (OBT) strategy, which has both proponents and detractors.
Leveraging large language models would seem to overcome one of the biggest challenges of using the one big table, which is the difficulty of discovery, pattern recognition, and its complete lack of organization. It’s helpful for humans to have a table of contents and well-marked chapters for their story, but AI doesn’t care.
Pros: Perhaps, finally, delivering on the promise of self-service data analytics. Speed to insights. Enables the data team to spend more time unlocking data value and building and less time responding to ad-hoc queries.
Cons: Is it too much freedom? Data professionals are familiar with the painful eccentricities of data (time zones! What is an “account?”) to an extent most business stakeholders are not. Do we benefit from having a representational rather than direct data democracy?
Who’s driving it: Super early startups such as Delphi and GetDot.AI. Startups such as Narrator. More established players doing some version of this, such as AWS QuickSite, Tableau Ask Data, or ThoughtSpot.
Practicality and value unlock potential: Refreshingly, this is not a technology in search of a use case. The value and efficiencies are evident–but so are the technical challenges. This vision is still being built and will need more time to develop. Perhaps the biggest obstacle to adoption will be the infrastructure disruption required, which will likely be too risky for more established organizations.
Data Product Containers
What it is: A data table is the building block of data from which data products are built. In fact, many data leaders consider production tables to be their data products. However, for a data table to be treated like a product, a lot of functionality needs to be layered on, including access management, discovery, and data reliability.
Containerization has been integral to the microservices movement in software engineering. They enhance portability and infrastructure abstraction and ultimately enable organizations to scale microservices. The data product container concept imagines a similar containerization of the data table.
Data product containers may prove to be an effective mechanism for making data much more reliable and governable, especially if they can better surface information such as the semantic definition, data lineage, and quality metrics associated with the underlying unit of data.
Pros: Data product containers look to be a way to better package and execute on the four data mesh principles (federated governance, data self-service, treating data like a product, domain first infrastructure).
Cons: Will this concept make it easier or more difficult for organizations to scale their data products? Another fundamental question, which could be asked of many of these futuristic data trends, is, do the byproducts of data pipelines (code, data, metadata) contain a value for data teams that is worth preserving?
Who’s driving it: Nextdata, the startup founded by data mesh creator Zhamak Dehgahni. Nexla has been playing in this space as well.
Practicality and value unlock potential: While Nextdata has only recently emerged from stealth and data product containers are still evolving, many data teams have seen proven results from data mesh implementations. The future of the data table will be dependent on the exact shape and execution of these containers.
The Endless Reimagination of the Data Lifecycle
To peer into the data future, we need to look over our shoulders at data past and present. Data infrastructures are in a constant state of disruption and rebirth (although perhaps we need some more chaos).
What has endured is the general lifecycle of data. It is emitted, it is shaped, it is used, and then it is archived (best to avoid dwelling on our own mortality here). While the underlying infrastructure may change and automation will shift time and attention to the right or left, human data engineers will continue to play a crucial role in extracting value from data for the foreseeable future.
And because humans will continue to be involved, so too will bad data. Even after data pipelines, as we know them, die and turn to ash, bad data will live on. Isn’t that a cheery thought?
This article was co-written with Barr Moses.
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