Cloud Data Warehousing: What It Is, Why It Matters

Most businesses have a wealth of data they do not know how to handle. There is customer relationship management data, transaction data, marketing activities data, enterprise resource planning data, the list never ends. What organizations need is not data, but meaningful data.

What Is a Cloud Data Warehouse?

The data warehouse is an information architecture designed for analytical purposes only. In contrast to an operational database, which is responsible for transactional processing, a data warehouse gathers information in a unified format for rapid and efficient querying.

On-site implementations of such systems have existed for many years already. However, they require considerable investments in maintenance, scaling, and adaptation in response to business requirements changes. Once a peak period comes to end, the infrastructure becomes underutilized.

The advantages of cloud data warehousing include the absence of capital expenditures on hardware for handling peak loads, scalability of resources according to actual demand, and pay-per-use pricing models. It also implies zero investments in server farm space, asset purchasing cycle management, and downtime windows.

How Cloud Data Warehouses Actually Work

A shift to the cloud is about much more than just relocating hardware. Cloud data warehouse providers today use architectures where compute and storage resources are completely separated from one another. This is a fundamental change that has a lot more impact than simply sounding impressive.

Traditionally, with on-premise solutions, getting faster queries involved buying more machines, along with extra storage capacity that was often unnecessary. In the cloud, however, compute and storage layers have been completely decoupled. You can launch compute resources when needed, run a heavy process, and turn off those resources afterward, leaving your storage untouched.

It also ensures that different teams are able to query the same information simultaneously without stepping on each other’s toes. The finance department performing their monthly report is not slowed down by the data science team who need to train their models using the same data set. Such workload segregation would have been quite difficult on-premises without heavy-duty engineering.

What Makes Cloud Warehousing Useful for Growing Businesses

Scaling is the buzzword here, but there’s more to migrating to a cloud-based data warehouse than just scalability. Let’s examine some of the often-overlooked advantages of using a cloud data warehouse.

Faster time to insights. Modern cloud data warehouses have integrations with tools like dbt, Fivetran, and Airbyte which allow your team to process the data much quicker to prepare it for analysis. If before you needed days, you will need mere hours after switching.

Less operational work. Your data engineers will be relieved of any infrastructure duties, and they will get back their precious engineering hours. It will be easier for them to build the pipelines and write queries without having to worry about anything else.

Easier collaboration. The cloud systems can be accessed remotely, which is essential since the data teams may be geographically dispersed. People in different time zones can work together in the same environment without having to use the virtual private network or login into a common server.

Better cost visibility. With on-premises solutions, costs are static, and you just don’t know how much it really costs. Cloud-based data warehousing focuses on usage and allows you to see exactly what a certain task or query will cost you and to optimize computing resources accordingly.

The Challenges You Should Know About

The cloud data warehousing approach does not provide a ready-to-use solution. There are several problems that need to be taken into account by companies.

Data migration complexity. The transition process from a traditional, in-house system to cloud-based data warehousing is usually not easy. The different schemas, incompatible types of data, and several years of accumulated SQL logic will definitely turn out to be your responsibility when moving the data. Working with a team that specializes in data warehousing services can save significant time here not because the work is impossible, but because the edge cases are numerous and specific.

Query cost management. Cloud storage centers bill based on the number of data entries that were scanned or compute time utilized. Malformed SQL statements can be quite costly. This necessitates an initial cost outlay in order to optimize queries, cluster data, and ensure proper access rights for analyses.

Governance and compliance. As the data makes its way to the cloud environment, questions arise regarding data location, accessibility, and retention period. Industries that are regulated, such as the financial sector, the healthcare industry, and insurance companies, will have to understand how the regulations fit into the cloud data warehousing configuration before going live.

Choosing the Right Platform

The three popular platforms in the realm of enterprise cloud data warehousing include Snowflake, BigQuery, and Redshift. Let us have a look at their benefits.

For one thing, Snowflake is platform-independent. It runs on AWS, Azure, and GCP alike, and its compute-and-storage separation architecture is quite polished. It also has a solid set of solutions designed to facilitate data sharing across organizations, which makes it particularly suitable for flexible environments where there is no need to depend on a specific provider.

On the other hand, BigQuery features close integration with the Google analytics ecosystem and a serverless model of operation. One does not need to control and provision compute clusters in any way. For an organization heavily engaged in GCP, it would make a lot of sense to try out.

Redshift is a product by Amazon and offers great integration with AWS. It would fit organizations whose workloads already use other services provided by AWS.

When to Actually Make the Move

Not all businesses require a migration to a cloud data warehouse. For example, if you’re operating a small company with properly organized transactional data and basic requirements when it comes to reporting, a well-maintained relational database should do.

However, if your data engineering staff has to spend more time maintaining infrastructure than developing data pipelines, your reporting processes get slowed down by many people querying at once, or it’s time for a hardware refresh cycle in your on-prem environment – these are signs that you should seriously consider moving to the cloud.

The organizations that benefit from the use of cloud data warehouses aren’t necessarily the fast movers. What sets them apart is the presence of a clear strategy, which includes knowledge about data sources and the ability to define necessary queries along with creating pipelines for a cloud data warehouse optimization.

For teams evaluating where to start, data engineering companies that provide data warehousing services can guide organizations at various stages of their journey, from initial architecture decisions to ongoing pipeline maintenance.

Cloud data warehousing has advanced significantly within the past five years. There is much more mature software, more extensive documentation, and many more integration solutions. For any data-based company, the issue is no longer about whether to migrate, but about how to avoid breaking things along the way.