When talking to companies about their data analytics needs, we frequently hear, "Which do I need: a data lake or a data warehouse?" Given the variety of data users and needs within an organization, this can be a tricky question to answer. The best solution depends on intended usage, types of data, and personnel.
But there’s more to the decision, so let’s discuss some of the key differences and organizational challenges of each.
If you know what big data you need to analyze, have a clear understanding of their structure, and have a known set of questions you need to answer, then you are likely looking at a data warehouse.
Data warehouses are often challenging to manage. Legacy systems that have worked well in the past 40 years have proven to be very expensive and pose many challenges around data freshness, scaling, and high costs.
Furthermore, they cannot easily provide AI or real-time capabilities without bolting that functionality on after the fact. These issues are not just present in on-premises legacy data warehouses; we even see this with newly created cloud-based data warehouses. Many do not offer integrated AI capabilities, despite their claims. These new data warehouses are essentially the same legacy environments but ported over to the Cloud.
On the other hand, if you need discoverability across multiple data types, are unsure of the types of analyses you’ll need to run, are looking for opportunities to explore rather than present preset insights, and have the resources to manage and explore this environment effectively, a data lake is likely to be more suitable for your needs.
Data lakes have their challenges. In theory, they are low-cost and easy to scale, but many of our customers have seen a different reality in their on-premises data lakes.
Planning for and provisioning sufficient storage can be expensive and complicated, especially for organizations that produce highly variable amounts of data. On-premises data lakes can be brittle, and maintenance of existing systems takes time. In many cases, the engineers who would otherwise be developing new features are relegated to caring for and feeding data clusters. More bluntly, they are maintaining value instead of creating new value.
Overall, the total cost of ownership is higher than expected for many companies. Governance is also not easily solved across systems, especially when different parts of the organization use different security models. As a result, the data lakes become siloed and segmented, making it difficult to share data and models across teams.
This requires convergence in both the technology and the approach to understanding and discovering the value in your data.
You can build a data warehouse or a data lake separately on Google Cloud Platform (GCP), but you don’t have to pick one or the other. In many cases, the underlying products that our customers use are the same for both, and the only difference between their data lake and data warehouse implementation is the data access policy that is employed.
In fact, the two terms are starting to converge into a more unified set of functionalities, a modern analytics data platform. Let’s look at how this works in GCP.
The BigQuery Storage API allows users to use BigQuery Storage like Google Cloud Storage (GCS) for several other systems, such as Dataflow and Dataproc.
This allows for breaking down the data warehouse storage wall and enables running high-performance data frames on BigQuery. In other words, the BigQuery Storage API allows your BigQuery data warehouse to act as a data lake.
So, what are some of its practical uses? For one, we built a series of connectors—MapReduce, Hive, and Spark, for example—so that you can run your Hadoop and Spark workloads directly on your data in BigQuery. You no longer need a data lake in addition to your data warehouse! Dataflow is incredibly powerful for batch and stream processing.
Today, you can run Dataflow jobs on top of BigQuery data, enriching it with data from PubSub, Spanner, or any other data source. BigQuery can independently scale both storages and compute, and each is serverless, allowing for limitless scaling to meet demand no matter the usage by different teams, tools, and access patterns.
All the above applications can run without impacting the performance of any other jobs accessing BigQuery at the same time. In addition, the BigQuery Storage API provides a petabit level network, moving data between nodes to fulfill a query request effectively leading to similar performance to an in-memory operation.
Serverless data solutions are necessary to allow your organization to move beyond data silos and into the realm of insights and action. All our core data analytics services are serverless and tightly integrated.
Change management can be a significant hurdle when introducing new technology into an organization.
At Crystalloids, we believe in empowering our customers, not locking them into proprietary solutions. That's why we leverage Google Cloud to provide open, flexible options for seamless integration. Whether it's connecting with existing on-premises environments, other cloud platforms, or even edge computing, we enable a truly hybrid cloud experience:
BigQuery Omni removes the need for data to be ported from one environment to another and instead takes the analytics to the data regardless of the environment.
Apache Beam, the SDK leveraged on Cloud Dataflow, provides transferability and portability to runners like Apache Spark and Apache Flink.
For organizations looking to run Apache Spark or Apache Hadoop, Google Cloud provides Dataproc.
Most data users care about their data, not which system it resides in. The most important thing is having access to the data they need when they need it. So, for the most part, the type of platform does not matter for users so long as they are able to access fresh, usable data with familiar tools—whether they are exploring datasets, managing sources across data stores, running ad hoc queries, or developing internal business intelligence tools for executive stakeholders.
As you can see, the traditional lines between data lakes and data warehouses are blurring. Modern businesses need a solution that combines the strengths of both, and that's exactly what Google Cloud offers.
With BigQuery at its core, this unified approach allows for flexible data ingestion, efficient processing, and powerful analytics – all within a scalable and cost-effective platform. This convergence empowers data scientists to seamlessly access and analyze data from various sources, including databases, data lakes, and data warehouses. They can leverage the unified platform to develop advanced analytics models, perform complex queries, and extract valuable insights from diverse datasets.
At Crystalloids, we're experts at helping businesses navigate this evolving data landscape. We can help you design and implement a modern data platform that meets your specific needs and unlocks the full potential of your data. Contact us today to learn more about how we can help you build a modern, unified analytics data platform.