Our organization is struggling with massive amounts of unstructured data from IoT sensors and social media. We currently use a traditional SQL warehouse, but it’s becoming too expensive and slow to scale. Should we be looking at a Data Lake approach with Hadoop or S3, or is a "Lakehouse" architecture the modern standard for 2025? I need to understand the trade-offs regarding data governance and query speed for our analytics team.
3 answers
Rebecca, the decision hinges on the "schema-on-read" versus "schema-on-write" requirement. Data Warehouses are great for structured data and fast SQL queries, but they fail at scale with unstructured logs. A Data Lake allows you to store everything in its raw format cheaply. However, the modern "Lakehouse" architecture—using technologies like Databricks or Snowflake—is the sweet spot. It provides the low-cost storage of a lake with the management and ACID transactions of a warehouse. For IoT data specifically, the Lakehouse model prevents your storage from turning into a "Data Swamp" where nothing is searchable.
Are you more concerned about the initial storage costs of the raw data, or the eventual latency your data scientists will face when running complex ML models?
Go with the Lakehouse. It’s the standard now because it supports both BI reporting and Data Science workloads on the same data footprint, reducing duplication.
I agree with Nancy. Eliminating the ETL overhead between the lake and the warehouse is the single biggest productivity boost a data team can achieve in 2025.
Steven, we are actually worried about both. Our current latency is killing our real-time reporting. We've started looking into Apache Iceberg to sit on top of our S3 buckets. It seems to provide that structure we need without the high cost of a proprietary warehouse. By addressing the "Metadata Gap," we’re hoping to keep our storage costs flat while giving the analysts a SQL-like experience that doesn't time out. It’s a tough balance to strike with 50TB of new data weekly.