Data Panda lifts data from your CRM, ERP, ecommerce, finance and product systems into S3 on a known schedule. Once it sits in one bucket structure, Athena, Redshift, EMR, Snowflake and your AI tooling all read the same files instead of each one keeping its own copy.
Amazon S3 is the object storage service that AWS launched in 2006 and has run continuously since. It holds objects inside buckets, addressed by a key, and the design target is simple: store any amount of data, retrieve it from anywhere on the internet, pay for what you use. AWS publishes a durability target of eleven nines (99.999999999%) and a default availability target of 99.99% on S3 Standard, with data replicated across multiple devices in multiple availability zones inside a region.
The service today carries hundreds of exabytes of customer data and handles more than 200 million requests per second on average, according to the AWS S3 product page. Around the core PUT and GET surface sit a stack of features that matter for analytics: storage classes that range from S3 Standard for hot data through Intelligent-Tiering, Standard-IA, Glacier Instant Retrieval, Glacier Flexible Retrieval and Glacier Deep Archive for colder tiers; Express One Zone for single-digit-millisecond latency; lifecycle rules to move files between classes automatically; versioning and Object Lock for recovery and WORM compliance; replication across regions and accounts; and IAM, bucket policies, Block Public Access and SSE encryption for governance. S3 Tables, the managed Apache Iceberg surface AWS added more recently, lets Athena, Redshift, EMR, Snowflake, Spark, Trino and DuckDB read the same lakehouse tables through the Iceberg REST Catalog without each engine writing its own copy.
BI tools and SQL engines read curated S3 prefixes instead of stitching across operational systems.
Data lands in S3 on a schedule that matches the business, not the loudest dashboard.
Bedrock, SageMaker and your own model code train and infer on the same files BI reads.
Internal apps, customer portals and partner exchanges read the same S3 lake.
A list of concrete reports, automations and AI features we have built on AWS S3 data. Pick the one that matches your situation.
We already use S3 for backups. Can the same account become our analytics lake?
Yes, and it is the path most BE/NL teams already on AWS take. The pattern is to set up dedicated buckets or prefixes for the lake (raw, staged, curated), keep them separate from the backup buckets via IAM and lifecycle rules, and load operational data into the raw zone on a schedule. Backups stay where they are; analytics gets its own zoned layout that BI and AI tools can rely on.
Should we land data as Parquet files or use S3 Tables with Iceberg?
Parquet in a partitioned layout still works for most reporting needs, especially when only Athena and one or two engines read the lake. S3 Tables make sense once multiple engines (Athena, Redshift, Snowflake, Spark) need to write to the same tables, when you want managed compaction and snapshot retention, or when you want the Iceberg REST Catalog as the shared interface. We pick per workload, not per fashion.
How do we keep S3 storage cost from growing forever as we add raw data?
Lifecycle rules and the right storage classes do most of the work. Hot partitions stay on S3 Standard, warm history moves to Standard-IA or Intelligent-Tiering, cold archive lands in Glacier Flexible Retrieval or Glacier Deep Archive depending on how often you need it back. Combined with 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 S3 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 S3 storage growth, request 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 AWS S3 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. AWS lets you pin buckets to a specific region, and objects in a region do not leave it unless you explicitly replicate them out. For BE/NL teams that means eu-west-1 (Ireland), eu-west-3 (Paris) or eu-central-1 (Frankfurt) for the lake, with Block Public Access on by default and replication scoped to other EU regions if you need geographic redundancy. Data-residency clauses in procurement contracts read cleanly against this setup.
Plain S3 with partitioned Parquet is enough for most reporting and for lakes read mostly by Athena. S3 Tables earn their place when several engines (Athena, Redshift, Snowflake, Spark, Trino) need to read and write the same tables with ACID guarantees, or when you want AWS to handle Iceberg compaction and snapshot retention instead of running it yourself. We pick per workload after we see the read pattern.
Lifecycle rules per prefix, the right storage class per access pattern, and versioning expiry on the raw zone. Hot partitions stay on Standard, warm history goes to Intelligent-Tiering or Standard-IA, cold archive lands in Glacier Flexible Retrieval or Glacier Deep Archive. We also watch request cost on Athena and EMR, because scanning whole prefixes instead of partitions is what drives most surprise bills, not storage itself.
We review your AWS S3 setup and the systems around it. Together we pick the first thing worth building.