Data Panda lifts data from your CRM, ERP, ecommerce, finance and product systems into Azure Blob on a known schedule. Once it sits in one container layout, Synapse, Fabric, Databricks and Snowflake all read the same files instead of each one keeping its own copy.
Azure Blob Storage is the object storage service that Microsoft made generally available in 2010 as part of Azure Storage. It holds objects inside containers, addressed by a name, and the design target is straightforward: store any amount of unstructured data, reach it from anywhere, pay for what you use. Microsoft publishes a durability target of eleven nines (99.999999999%) on geo-zone-redundant configurations and a default availability target of 99.9% on the Hot tier, with read access geo-redundant variants reaching 99.99%.
Around the core PUT and GET surface sit a stack of features that matter for analytics: three blob types (block blobs for general object storage, append blobs for log workloads, page blobs for VM disks); access tiers from Hot through Cool and the newer Cold tier added in 2023, down to Archive for rarely read history; redundancy options from LRS and ZRS inside one region to GRS, RA-GRS, GZRS and RA-GZRS that replicate across regions; lifecycle management rules that move blobs between tiers automatically; soft delete, versioning and immutable storage for recovery and WORM compliance; private endpoints, RBAC, customer-managed keys and Azure AD authentication for governance. Azure Data Lake Storage Gen2, the variant with hierarchical namespace turned on, layers a real directory tree and POSIX-style ACLs on top of the same storage account, which is what Synapse, Fabric, Databricks and Snowflake external tables read against when teams use Azure as their lakehouse foundation.
Power BI, Fabric and SQL engines read curated containers instead of stitching across operational systems.
Data lands in Azure Blob on a schedule that matches the business, not the loudest dashboard.
Azure OpenAI, Azure ML and your own model code train and infer on the same files BI reads.
Internal apps, customer portals and partner exchanges read the same Azure lake.
A list of concrete reports, automations and AI features we have built on Azure Blob Storage data. Pick the one that matches your situation.
We already use Azure Blob for backups. Can the same subscription become our analytics lake?
Yes, and it is the path most BE/NL teams already on Azure take. The pattern is to spin up a dedicated storage account with hierarchical namespace turned on (so ADLS Gen2 features are available), keep it separate from the backup account via RBAC and lifecycle policies, and load operational data into the raw zone on a schedule. Backups stay where they are; analytics gets its own zoned layout that Synapse, Fabric, Databricks and Power BI can rely on.
Should we land data as Parquet files or use Delta tables on ADLS Gen2?
Parquet in a partitioned layout still works for most reporting needs, especially when only Synapse Serverless and one or two engines read the lake. Delta starts earning its keep once Databricks or Fabric write back to the same tables, when you want ACID guarantees on multi-writer workloads, or when you rely on time travel for audit and rollback. We pick per workload, not per fashion.
How do we keep Azure Blob storage cost from growing forever as we add raw data?
Lifecycle management rules and the right access tiers do most of the work. Hot partitions stay on Hot, warm history moves to Cool, rarely read data drops to the Cold tier added in 2023, and long-term archive lands in Archive with rehydration when you really need it back. Combined with soft delete and versioning expiry on the raw zone, the bill follows business value rather than calendar time.
The CFO gets reporting that ties to the boekhouding because the underlying numbers come from one curated ADLS Gen2 zone. Revenue, margin and AR carry one definition, sourced from the same lake the sales board reads, so the close stops being three people reconciling exports.
Sales leaders see pipeline, forecast and quota next to invoiced revenue and product usage on lake-grade data. The same numbers travel to the QBR pack, the standup and the steering committee without copy-paste from a spreadsheet.
Operations and data leads track storage growth, transaction cost and lifecycle transitions in one view. The bill becomes predictable, and the lake stops growing sideways with team-specific copies of the same source files.
You keep the reporting tool you already have. We connect it to the warehouse where your Azure Blob Storage data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Yes. Azure storage accounts are pinned to a specific region, and objects in a region do not leave it unless you explicitly configure object replication to another region. For BE/NL teams that means West Europe (Netherlands), North Europe (Ireland) or France Central for the lake, with private endpoints in front and replication scoped to other EU regions if you want geographic redundancy through GRS or GZRS. Data-residency clauses in procurement contracts read cleanly against this setup.
A plain blob account is enough for backups, document drops and simple object workloads. ADLS Gen2 (a storage account with hierarchical namespace turned on) earns its place once analytics tools enter the picture, because it gives you a real directory tree, atomic rename of folders, POSIX-style ACLs and the abfss:// driver that Synapse, Fabric, Databricks and Snowflake all expect. We turn it on from day one for the lake account.
Lifecycle management rules per container, the right access tier per pattern, and versioning and soft-delete retention sized per zone. Hot partitions stay on Hot, warm history goes to Cool, rarely read data lands on Cold, long-term archive sits in Archive. We also watch transaction and read cost on Synapse and Databricks, because scanning whole containers instead of partitions is what drives most surprise bills, not storage itself.
We review your Azure Blob Storage setup and the systems around it. Together we pick the first thing worth building.