Data Lakehouses: How Secure is the Best of Both Worlds?

As we enter a new cloud-first era, advancements in technology have helped companies capture and capitalize on data as much as possible. Deciding between which cloud architecture to use has always been a debate between two options: data warehouses and data lakes. But a new concept has emerged, called the data lakehouse, that has addressed the growing pain points of having two separate infrastructures, begging the question: should we make the switch? Can we have the best of both worlds? And most importantly, how secure is our data with this new infrastructure?

Data Warehouse: The Analytics Workhorse

A data warehouse is a type of relational database that is specifically designed for data analytics -- i.e., reading large amounts of data and understanding trends and relationships across the data. That’s why data warehouses have become a core part of many enterprises’ business intelligence and analytic teams.

Structured data gets loaded from production databases (that are optimized for fast writes) into data warehouses via ETL (extract, transform, load) processes. Structured data refers to any clearly defined and searchable data type, such as phone numbers, dates or even text strings like names, and are highly understood by machine languages. However, as demand for data ingestion has increased, traditional data warehouses have become an expensive storage option for those looking to scale. Data warehouse solutions include Azure Synapse Analytics and Google BigQuery.

Data Lake: The Massive and Cheap Data Store

Data lakes emerged in 2010 as a cost-effective solution to address the boom in big data. Unlike data warehouses, data lakes simply store data, like the hard disk and file system of a laptop. Data lakes store all types of data, including structured, semi-structured and unstructured data. Semi-structured data is contained in non-tabular files, but the files still contain tags or other markers to separate semantic elements and enforce hierarchies of records and fields. Examples of semi-structured data include XML, JSON, and CSV files. Unstructured data refers to any data in its native format that remains undefined until needed. This includes pictures, video files, and even social media posts.

However, due to their massive storage capacity, data lakes can potentially become data swamps – wastelands of raw, unusable data without any clear organization or active management. In addition, because the data isn’t formatted for analytical use, the process of preparing and driving value out of the data is both time-consuming and cumbersome for analytical teams. Data lake solutions include Amazon S3, Azure Data Lake, and Google Cloud Storage.

Data Lakehouse: The Best of Both Worlds?

Since the advent of data lakes, many enterprises have had both a data lake (for data storage) and a data warehouse (for data analysis). This dual-pronged solution has worked, but has the key cons of (a) time-consuming data transforming and loading process from data lake to data warehouse and (b) duplicate data in the data warehouse and data lake, which increases storage costs.

It’s no surprise that a new hybrid solution has emerged. The data lakehouse combines the best of both worlds. Data lakehouse solutions store data in a data lake, taking advantage of the data lake’s low cost storage. When data in the data lake is then needed for analysis, the data in the data lake is accessed directly for data management. Data lakehouses unify data movement across one infrastructure and remove data duplication (and cost), giving companies greater ability to scale. Data lakehouses can improve data quality and data governance while empowering BI and reporting. Data lakehouse solutions includeSnowflake, Amazon Redshift Spectrum, and Databricks.

Benefit Summary: