Build and Design a Data Lakehouse on Google Cloud Platform

Data is king in today’s modern world and the driving force behind many technological innovations, like Artificial Intelligence and Machine Learning. Google is no stranger to data, given the massive amount of information that goes through its systems every second. Their solutions on Google Cloud Platform (GCP) are a testament to this.

Let us look at one of the most popular data management solutions on GCP, which is data lakehouse.

What exactly is a data lakehouse?

Initially, enterprise companies embraced the concept of a data lake which allowed them to aggregate all their different data silos into a centralized location regardless of format. You could build a data warehouse on top of this solution for running detailed analytical queries and gaining meaningful sight into your data. However, data warehouse solutions did have limitations, such as a dependency on data to be transformed before it could be loaded into the platform, which was a time-consuming and costly exercise.

This is where a Data Lakehouse comes in.

The Data Lakehouse can be considered the next step or the next-gen of the data lake and the data warehouse concept by providing a single platform for data storage (regardless of format ) and data processing. By removing the data dependency to be in a specific form before being loaded, the data lakehouse allows for faster, real-time processing and improved scalability as it can process a much larger dataset.

Build a Data Lakehouse on GCP

If you want to start building a data lakehouse on GCP, you simply can use Google Cloud Storage and BigQuery to store the data. These services can exchange data with each other. It is highly recommended to store structured data on BigQuery and unstructured and semi-structured data, which you should store in Google Cloud Storage. After you build your data lakehouse, you can implement Vertex AI and import the data from BigQuery, and then, we can build machine-learning models for forecasting.

Some of the key advantages that a data lakehouse provides are:

  1. Storage and Processing power: By having the full force of the GCP platform behind it, companies do not need to worry about scalability and storage. Data Lakehouse can handle massive amounts of data from any number of data sources quickly and scale as per the company’s requirements.
  2. Better pricing model: The cloud’s pay-as-you-go model can be more appealing for companies than the full-on investment in an on-prem architecture.
  3. Support for more data formats: As we mentioned, Data Lakehouses can accommodate data in both structured and unstructured formats allowing for more flexibility.
  4. GCP offers several advanced security features, such as Identity and Access Management and Governance tools, to give cybersecurity teams visibility into their data stores

At the same time, however, companies also should keep the following into account before they move into data lakehouse:

  1. There is a learning curve for Data Lakehouse, which must be considered. Without the required expertise, companies may not see the benefit immediately emerge from their investment.
  2. Data Lakehouse is only as good as the data you put in it. Appropriate data quality and cleansing procedures must be in place, especially when dealing with many data sources, which can be time-consuming.
  3. GCP operates on a shared responsibility model, and customers need to make sure they are aware of their obligations regarding the data. It is essential to ensure cybersecurity teams are properly upskilled and data residency requirements have been cleared with the legal team before data is loaded.
  4. While the pricing model can seem appealing, companies must keep a close eye on their cost and understand the pricing model. Cloud costs, if not appropriately managed, can compound over time and become a drain on the company’s budget.

The future of data is in the cloud.

All major cloud hyperscalers such as GCP, AWS, and Azure know that the future of data is in the cloud, as enterprise companies simply do not want to invest in the infrastructure for managing massive data stores. Each cloud provider is offering its data lakehouse services or equivalent to attract customers.

Companies should consider the advantages and drawbacks we mentioned about data lakehouse before making an informed decision with stakeholders like data governance, legal, regulatory compliance, and of course, the cybersecurity team. Data is the key to success in the coming world and choosing an appropriate technology stack can be the main deciding factor between success and failure in the battle for market success!

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