The top 5 data warehouse trends in 2021 to look out for are the following: the rise of SaaS, on-demand analytics, on-demand visualization, and on-demand ML. The overarching theme in these trends is that data warehouses are now largely cloud-hosted with hybrid functionalities.
In 2021, we can expect a heavier emphasis on multi-use cloud data warehouse solutions that offer on-demand functionalities. For instance, a popular trend is to create AI/ML models on-the-fly instead of exporting data to a different solution where the model will be instantiated. Feature hybridization results in more democratic, efficient, and cost-effective warehousing solutions.
A Data Warehouse is defined as an enterprise system where structured, unstructured, and/or semi-structured data is stored for analysis and visualization. Common use cases include point-of-sale transactions, marketing automation, customer relationship management, and virtually any queryable data.
Data warehouses are commonly hosted on the cloud. Though it is possible for businesses to have a data warehouse on-premises, the more cost-effective, reliable, efficient, and scalable solution is to host it on the cloud.
These services are usually serverless/NoOps, thereby reducing OpEx. Also, since they are hosted on the cloud, CapEx is automatically reduced because of the lack of hardware investment. To cap it all off, cloud data warehouses usually have flexible pricing depending on the service provider. There is a myriad of cost-effective options like pay-as-you-use and up-front investment.
As mentioned above, using Cloud Data Warehouses are usually serverless/NoOps. This means that the hassle of maintaining hardware and underlying software will fall to service providers. According to their use case, the end user's role is to design the right infrastructure and hit the proper data requirements.
Cloud Data Warehouses can leverage the robustness of cloud service solutions. For instance, compared to on-premise data warehouses, they are not prone to service outages that may incur massive business costs. Next, cloud service providers' SLAs include guarantees of low-latency (< 100-200ms on average) and multi-region read/fail-over replication.
Replication refers to having several copies of an instance ready and available--ideally across different regions.
Anomalies in well-scrubbed data are usually very rare when hosted in cloud data warehouses. This is because a copy of the ground truth exists and is cross-validated across instances to ensure data consistency. Imposing data standards is extremely important as it dictates the flow of business processes and, consequently, business decisions.
Data Warehouses today are designed to work seamlessly with AI/ML. It is assumed by service providers that eventually, data will be used to create models that will help with predicting business outcomes and drive business decisions.
Having standardized and high-quality data on-hand for executives and decision-makers is extremely advantageous for business intelligence. There is a full range of historical data is instantly available for perusing as executives ponder their next moves. ML predicted data/outcomes are also readily available after a few minutes of training and predicting based on current data. This allows key figures to make data-driven decisions that are empirically good for the business.
Data warehouses are usually at the end of the pipeline in the grand scheme of things after data has undergone the appropriate ETL operations. However, there are cases where ETL is not required, and data is ingested directly to the data warehouse.
Once data has been ingested by the data warehouse, visualization becomes possible since data dashboards can now be presented to stakeholders visually appealing yet factually accurate. Most major cloud data warehouses today have built-in connectors to visualization software. For instance, BigQuery, GCP's de facto data warehouse, and analytics solution can be connected to Data Studio, GCP's easy to use visualization platform.
The three most popular data warehousing is GCP's BigQuery, AWS' Redshift, and Snowflake. These three data warehouse solutions offer similar functionalities and have the most extensive user base to date. They differ slightly in the warehouses' geographic locations, the volume of data, data source, dedicated human resources for support and maintenance, scalability, security, and pricing models.
A company called Zaloni offers data management software. Their flagship product, Arena, is meant to be an abstraction of various cloud services. The arena can perform various DataOps such as end-to-end data ingestion service, metadata catalog, self-service provisioning, and data governance. We see that data warehouses can be managed with external sources to streamline data operations further.
Traditional data pipelines relied on ETL (Extract Transform Load) because early cloud technologies were not mature enough to ingest data as-is. Now, cloud technologies have evolved to the point of having intelligent schema detection systems to allow as-is ingestion. For instance, traditional data pipelines had to manually convert file types before feeding them into the data warehouse.
ELT stands for Extract, Load, and Transform. Here, we see a reversal of steps wherein the loading comes before the transforming. This is because modern data warehouses are now smart enough to do fundamental transformations before data is ingested, such as schema detection. This particular change marks the shift from "schema on write" to "schema on reading."
Moreover, modern data warehouses can do complex transformations in-place--without having to feed the data into a different transform. In essence, basic analysis can be done in one step--the data warehouse. This trend is foreseen to continue into 2021, where cloud service providers aim to create highly automated and intelligent cloud services.
Building on the trend from ETL to ELT, on-demand analytics, visualization, and ML represent some of the complexes transforms that can be done to data within the data warehouse. This trend is a consequence of the shift from ETL to ELT because, in most cases, the Transform part of the pipeline can be done in the data warehouse.
Business Intelligence analytics can now be done via the data warehouse. An example would be if e-commerce data were housed in GCP's BigQuery. Let's say that each CSV file contains one day's worth of data. In particular, this data includes information about customer behavior on the site, such as how long they looked at a specific page, how long it took them to make a purchase, how many times they doubled back to a particular page, etc.
Now stakeholders will likely want to know how to optimize the site's interface and offerings to encourage customers to make purchases. To figure this out, we can find a few essential things out. Using BigQuery, I will input a query that lets me know which item was the most returned-to page with the least purchases.
We can hypothesize that perhaps the product is attractive to customers, but the price is much too high. To further investigate, we can query for a product in direct competition with the product in question that has the most purchases. If it turns out that it is cheaper than the product in question, then that points to strong evidence that our hypothesis of it being attractive but too pricey might be right.
So, our stakeholders then decide to put the product in question on sale. If its sales increase, that's great. It means our hypothesis was indeed correct. However, if it did not change anything, we can keep fishing for clues about what to do. BigQuery is extremely powerful, and with the right data, business decisions can be made data-driven in no time.
Building on the previous example, let's say our stakeholders would like a data dashboard of the site's top-selling products. That's easy to do with Google Data Studio because data from BigQuery can be directly exported to Data Studio. What's even better is that as the data in BigQuery changes, so too does the data in Data Studio. This means that it is updated in realtime!
At this point, all we need to do is take the top 10 best-selling products of all time. We need to collate all data files (remember that each file corresponds to one day) and sort the products in descending order.
We then export this to Data Studio, and with minimal effort, we now have a realtime dashboard of the site's best-selling products of all-time to present to our stakeholders.
Regardless of cloud service provider, modern data warehouses now have some way of connecting to an external or internal visualization tool.
Going back to the e-commerce example, let's say that our stakeholders have instructed the data team to construct a linear regression model that predicts which product would be the top-selling product for each day. Thanks to built-in data warehouse ML, creating a linear regression model is as easy as writing a 1-3 line query.
The model is then saved within BigQuery and is ready for usage anytime. We can query the model once a day to make a prediction for the next day and see how accurate it is. Any tweaks can be easily done within BigQuery if it is not as precise as the stakeholders hoped.
Sustainability is not confined to just data warehouses. Recently, alarming climate change has called the attention of governments and technocrats alike. Increasing concern for climate change has urged tech figureheads to push for green computing.
In particular, renewable energy is touted as one of the best ways to combat climate change. Data Centers worldwide are quickly adopting renewable energy for several reasons. First, 15% of a warehouse's total operating budget costs come from energy. Second, carbon emissions from traditional energy sources are one of the leading causes of climate change.
Google Data Centers have purchased enough renewable energy to match 100% of their annual global electricity consumption in 2019. Most of this energy comes from wind and solar farms. By 2021, renewable energy capacity additions are expected to increase by 10%.
Even with the ubiquity of cloud-based environments, many companies still face the challenge of migrating legacy processes to the cloud. In most cases, these decades' old methods are challenging to modernize. As such, many companies who have similar dilemmas choose to go for hybrid deployments instead of fully cloud-based deployments.
Though cloud-based deployments are more cost-effective, reliable, and scalable, on-premise deployments are still relevant to companies that need total control and accessibility over their ecosystem. Hence, as organizations shift to the cloud, many others will choose to use hybrid deployments first.
A Data Lakehouse is a combination of a Data Warehouse and Data Lake. This is an advanced version of what was discussed in Trend #2. The concept of a Data Lakehouse is that data is stored in the solution (as with a Data Lake) and processed in the same solution (as with a Data Warehouse).
The concept of a Data Lakehouse effectively reduces the layers in a data pipeline to just one layer. Data is ingested and housed in the Lakehouse and, at the same time, processed and analyzed. Here are the features of a Data Lakehouse, as listed by Snowflake:
Here at Codvo.ai, we are committed to designing a modern cloud ecosystem that is cost-effective, reliable, and scalable. More than that, we believe that data warehouses should be used as tools to provide business intelligence in a very short amount of time. Stakeholders should be in-the-know 24/7, and we design data warehouses that enable on-demand analytics, visualization, and machine learning. In today's business landscape, data-driven decisions can make or break a business.
If you are interested in building a modern cloud ecosystem, contact us today at firstname.lastname@example.org!
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