There’s a specific moment in every scaling manufacturer’s history when the ERP that ran the business perfectly at one size becomes a bottleneck at another. Query times stretch from seconds to minutes. Reports that used to run overnight now time out. Someone builds a workaround, then a workaround for the workaround. The organization starts losing trust in the data — not because the data is wrong, but because it takes too long to get and too much can go wrong along the way.
This isn’t a failure of the ERP. The ERP is doing its job. What’s failing is the data architecture around it.
Why Your ERP Isn’t Built to Be an Manufacturing Analytics Engine
This is the misunderstanding at the center of most manufacturing data problems. An ERP system is designed to record transactions and manage operations. It is not designed to handle complex analytical queries across years of historical data, support multiple concurrent users running cross-functional reports, or serve as the foundation for AI and machine learning workloads.
When manufacturers try to use the ERP as their analytics engine, they end up with two problems. First, the analytical queries degrade the operational performance of the system — the same database that’s recording live production events is trying to process a three-year inventory analysis at the same time. Second, the analytical capabilities are limited by the ERP’s data model, which is optimized for transactions, not for the kind of flexible, cross-functional analysis that modern management teams need.
How Data Lakehouse Architecture Solves the Analytics Bottleneck
The modern approach separates the operational layer from the analytical layer. The ERP handles transactions. A dedicated data infrastructure — built on technologies like Dremio and Snowflake — handles analytics, reporting, and the growing demand for real-time visibility.
Dremio is the layer that eliminates ETL complexity. Instead of building and maintaining fragile pipelines that move data from the ERP into a separate analytics environment, Dremio queries data directly where it lives — in the ERP, in production systems, in cloud storage, in any source — using standard SQL. A production planning team that needs to analyze three years of inventory data alongside current open orders gets that query in seconds, without anyone having to build a pipeline first.
Snowflake provides the cloud-scale warehouse layer for historical data at volume, cross-functional reporting, and the analytical foundation that AI workloads require. When a manufacturer is ready to build predictive models — demand forecasting, quality pattern detection, equipment maintenance prediction — Snowflake is the environment where those models run reliably at scale.
Together, this is what’s called a Data Lakehouse architecture: the flexibility of a data lake with the performance and governance of a data warehouse, without the ETL complexity that made traditional approaches so fragile.
Why Implementation Expertise Matters for Dremio and Snowflake
Dremio and Snowflake are not plug-and-play tools. The value is real — Henkel used Dremio to improve equipment efficiency across 250 manufacturing lines; Shell uses it to process billions of production records for forecasting — but that value only materializes when the implementation accounts for how your specific ERP structures data, what your reporting patterns actually look like, and how your internal team will operate the environment after go-live.
SIT has implemented both Dremio and Snowflake in Israeli manufacturing environments. We know where the configurations get complicated, which ERP integrations require custom work, and how to build a data lakehouse architecture that your team can actually operate and extend over time.
The ERP that got you here isn’t the problem. The missing analytical layer is. And building it correctly doesn’t require replacing anything — it requires adding the right backbone above what you already have.
Ready to break through your ERP’s analytical limits? Contact SIT today to learn how our expertise in Dremio and Snowflake can transform your data infrastructure into a high-performance engine for analytics, AI, and real-time decision-making. Let us show you the path to a future-proof data architecture — without disrupting what’s working.
