How Does The Data Lakehouse Help Customers With Their Data

What is a customer data stack, and how does it work?

We talk a lot about the current data stack, but there's a distinction to be made here because customer data is unique. It has a one-of-a-kind value proposition for the company, as well as a one-of-a-kind set of technical hurdles.

Data Engineering Solutions is valuable since it is the primary source of behavioral information about a company's clients. Anyone who has ever started a business knows that if you don't know your customers, you'll go out of business quickly. This is especially true for current online firms, where face-to-face interactions with customers are uncommon.

Customer data, like all important things, comes at a price. Remember those one-of-a-kind technical difficulties I described earlier? Let's look at the qualities of client data that make working with it challenging.

• Customer information is plentiful: Every day, modern business, which is fueled by internet transactions, generates a vast amount of data. Just ask anyone who has worked for a huge B2C firm like Uber or even a medium-sized e-commerce firm. Even basic interactions generate a tremendous amount of data.

• Customer data is incredibly noisy: The customer journey always entails a number of actions, yet not all of them are valuable. The problem is that there's no way of knowing what's valuable and what isn't until the data is analyzed. Your best bet is to keep track of everything and let your data analysts and scientists shine.

• Customer data is constantly changing: after all, no one has the same behavior for the rest of their lives, right? I mean, only the dead have conduct that does not change with time. This implies you'll need to constantly collecting large volumes of data, yet only portion of it will be useful at any given time.

• Customer data is a time series with multiple dimensions: This may sound extremely scientific, but all it implies is that time ordering is crucial, and each data point does not have a single value. This increases the data's complexity and how you interact with it. If you want to dig down a rabbit hole with this one, you may read about how we implemented our queueing system using PostgreSQL.

• Customer information might range from highly structured to fully unstructured: Client data, for example, is an invoice sent at a certain moment for a specific customer. A customer's engagement with your website is the same. Consumer data includes even the photo of that customer taken as part of the verification process.

What is a Data Lakehouse, and how does it work?

If you've been paying attention recently, you've probably heard a lot about data lakes and data lakehouses. The data lakehouse is even newer than the data lake, which is, admittedly, rather old! With the introduction of HDFS and Hadoop, humans began to create data lakes. That old, indeed. To create a data lake, all you need is a distributed file system and Map Reduce.

When it comes to data engineering services, obviously a lot has changed since the early 2000s. The term "data lake" has been officially coined, and the lakehouse is the newest child on the block.

Data Lake

Let's start with an explanation of what a data lake is. Clearing this up initially will aid our understanding of the data lakehouse.

The separation of storage and processing is the core notion behind a data lake. Is this something you've heard before? Snowflake and Databricks both talk about it a lot. But, as you may have noted, I mentioned HDFS and Hadoop at the start of this section. The first data lakes separated storage (HDFS) from processing (Map-Reduce) by employing HDFS as a distributed file system and Map-Reduce as a processing framework (Hadoop - MR). A data lake's essential concept is this.

When we think of a data engineering solutions nowadays, the first thing that comes to mind is S3, which takes the role of HDFS as the storage layer and provides storage to the cloud. Of course, we may use GCP and Azure's equivalents in place of S3, but the concept remains the same: an incredibly scalable object storage system that is accessible by API and resides in the Cloud.

Since Hadoop, processing has progressed as well. First, there was the development of Spark, which provided a more user-friendly Map-Reduce API, and then there were distributed query engines like Trino. Most of the time, these two processing frameworks coexist, addressing different needs. Trino is mostly used for analytical online queries where latency is critical, whereas Spark is primarily utilised for larger workloads (think ETL) where latency is less critical and data volume is significantly larger.

Data Lakehouse

A Lakehouse is an architecture that builds on top of the data lake notion and improves it with database-like capabilities. The data lake's constraints prompted the development of a variety of solutions, including Apache Iceberg and Apache Hudi. These technologies define a Table Format that may be layered on top of storage formats such as ORC and Parquet to provide additional functionality such as transactions.

What is a data engineering services, exactly? It's an architecture that combines the data warehouse's important data management characteristics with the data lake's openness and scalability.

Conclusion

If you're still reading, you've probably figured out why we need and utilise all of these keywords, and, more crucially, how they fit together.

Lakehouses and data lakes are rapidly evolving into full-featured data warehouses with infinite scalability and minimal cost. Customer information is an obvious fit for this type of storage and processing layer. These designs will enable you to accommodate any future use case, even if all you're interested in right now is operational analytics.

The data lake and lakehouse architectures are major advocates at RudderStack. We've spent a lot of time and money developing best-in-class integrations with data lakes and lakehouses like Delta Lake. You can create a comprehensive customer data stack that scales incredibly well and allows you to do anything you want with your data by combining RudderStack with a Lakehouse or data lake.