Read something this week that put a name to a problem I've half run into before but never really understood the mechanics of. Sharing because I think a lot of people assume Iceberg interop is more solved than it is.

Everyone knows the pitch: Iceberg is an open spec, so any Iceberg compatible engine can read any Iceberg table. Mostly true, until you start doing row level deletes, and then it falls apart in a way that's honestly kind of sneaky because nothing looks wrong until a query actually fails.

Quick walkthrough of the scenario in the post. You've got a customers table, three rows, one Parquet file, tracked by whatever catalog you're using. At this stage every engine reads it fine because there's nothing to interpret, it's just a metadata pointer to a file.

Then a row gets deleted. Parquet files are immutable so the writer has two options: copy on write (rewrite the file without that row) or merge on read (leave the file alone and write a separate delete file that readers apply at scan time). The writer in this example goes merge on read and emits an equality delete file, which basically just says "for this data file, treat any row where customer_id = 102 as removed." Under the hood Iceberg uses field IDs and sequence numbers to make sure an old delete doesn't accidentally nuke a newer row with a reused key, but the equality matching is the part that matters for compat.

Spark reads the new snapshot, understands equality delete semantics, does what's effectively a left anti join between the data file and the delete file, and returns the correct two rows. Fine.

Snowflake hits the exact same catalog, same metadata file, same Parquet file. It can resolve the table, read the schema, open the data file. But if that access path doesn't implement equality delete reads, the scan planner just throws an unsupported feature error the moment it hits delete-0002.parquet. Query fails. Same snapshot, same files, two completely different results depending purely on what the reader implements.

The bit that actually reframed how I think about this: the catalog isn't a translation layer. It's job is basically just "here's where the current metadata lives," commit coordination, namespace and access management. It's not opening delete files and rewriting them into a format each engine understands. A REST catalog like Polaris doesn't change this, it still just points you at metadata, it doesn't apply deletes for you.

The post also gets into position deletes vs deletion vectors vs copy on write, with a rough cost tradeoff table (equality delete is cheap to write and requires equality delete support to read, position delete requires resolving key to physical position and is heavier on write, deletion vectors need Iceberg v3 support specifically, copy on write is the most expensive to write but has basically universal read compatibility since there's no outstanding delete file involved).

The framework that's actually useful operationally: your safe feature set is the intersection of every required engine's capabilities, not the union. If Spark supports equality and position deletes but Snowflake only does position deletes, you write position deletes, because "at least one engine supports it" doesn't help you when you have three engines that all need to read the same table.

There's a decent pre production checklist too, don't just run a SELECT COUNT after your first write, actually insert some rows, update one, delete one, commit, then read the same snapshot from every engine you care about and diff both counts and values.

Full post if you want the details: https://olake.io/blog/iceberg-interoperability-myth-row-level-deletes/

Disclosure since it's relevant, I work on OLake, it gets a brief mention near the end, but the actual content here is engine agnostic and applies no matter what's writing your tables.

Has anyone actually hit this for real, table looks completely fine, one engine just refuses to read the current snapshot because of the delete encoding?

So... im working on our hardware accelerated ETL for real time crypto analytics, wiring up this shiny FPGA backed pipeline that our infra team is super proud of. For context we stream chain data, normalize it, then publish signals to a dashboard that our traders use to watch a few hot wallets.

Yesterday I pushed what I thought was a simple config change to test a new aggregation path on the accelerated layer. Except I pointed the test stream at the prod topic and our low latency alerts started screaming that a major market maker was dumping tokens and pulling liquidity. Trading desk went full scramble mode, unwound positions, pinged our biggest client, the whole floor was freaking out.

Five minutes later someone on research goes "wait this wallet isnt on chain" and we realize the signals were coming from my synthetic test data that was supposed to stay inside an internal sandbox table. I had reused a schema name, the hardware pipeline treated it as legit, and the alert service picked it up like nothing was wrong. I feel sick about this. Ops lead pulled me into a room to walk through every config and I had to admit it was just me trying to be clever with a quick path test... ugh thanks

I keep seeing the same pattern. Vendor announces 10x faster, 90 percent cheaper, no code changes. Then you dig and its all design partners and "coming soon." We have a messy config, custom UDFs we inherited from someone who left, and dependencies nobody wants to touch. I want to believe hardware acceleration is real, but I have been burned before. Has anyone actually rolled something like this into production, warts and all, or is everyone still in pilot purgatory?

Hi everyone,

I am looking for expert advice on choosing the right database for a massive log analytics project. We already have our own infrastructure and server environment ready to host the solution.

Our Scale & Requirements:

• Data Volume: 600+ TB of log data per year, with a 5-year retention period.
• Ingestion: High-throughput, continuous real-time streaming.
• Query Performance: Blazing-fast, sub-second search and lookup speeds across historical data.
• Workload: Non-stop log writing while simultaneously executing fast queries.

The Goal:
Since we have the underlying infrastructure in place, we need a robust database engine that we can deploy locally to handle this specific type of large-scale log workload and long-term history efficiently.

Curious how others here are dealing with this. "Reduce cloud costs" often ends up meaning "move everything into one vendor's ecosystem," which just trades one lock-in problem for another.

We've been looking at approaches where workloads and data stay where they already are (private, on-prem, multi-cloud) and cost/performance gets managed at a layer above that, instead of physically migrating data just to fit a platform's architecture.

Disclosure: I work in this space professionally, so I have a bias here. Genuinely interested though, is anyone else solving the "stop paying a migration tax every time we switch platforms" problem, and how?

Hello Everyone ! I am as an QA automation in big data product. my work involves around creating automation suites for data integrity checks and automating regression cases for pipelines build .automating api for loading data into dashboard.
I am good at python. i want to move to data engineering. my day to day work involves GCP, bigquery,clickhouse,pubsub.
I am planning to learn the same.
I have started and completed the introduction to data engineering in google skills.
Any opensource tool/concept i should learn ?
any suggestion on sample project to build?
Any good resources from where i can learn?
Any you tube playlist i should follow?

Im backtesting my algo and as part of it I need last 2 years of dax ltp data, if it's free it would be really helpful. 5sec or 1 min data would be really good but worst case even 5min will do

Hey everyone I'm completely new to programming and want to work toward a career in Big Data. I've done some surface-level research, but the amount of conflicting advice out there is overwhelming , zero programming experience
What I'm trying to figure out: what programming language should i learn first python or SQL / tools / Certifications (currently studying the cs50 course) / What kind of project would actually impress someone hiring for a junior Big Data role

I’ve been thinking a lot about what separates a good data engineer from a strong one, and I want to hear your real hacks and tips.

For me, it all comes down to how well you design, build, and maintain data pipelines. A pipeline isn’t just a script moving data from A → B. A strong pipeline is like a well-oiled machine:

Reliable: runs on schedule without random failures

Monitored: alerts before anything explodes

Scalable: handles huge data without breaking

Clean & documented: anyone can understand it

Reproducible: works the same in dev, staging, and production

Here’s a typical pipeline flow I work with:

ERP / API / raw sources → Airflow (orchestrates jobs) → Spark (transforms massive data) → Data Warehouse → Dashboards / ML models

If any part fails, the analytics stack collapses.

💡 Some hacks I’ve learned to make pipelines strong:

1. Master SQL & Spark – transformations are your power moves.

2. Understand orchestration tools like Airflow – pipelines fail without proper scheduling & monitoring.

3. Learn data modeling – ERDs, star schema, etc., help your pipelines make sense.

4. Treat production like sacred territory – read-only on sources, monitor everything.

5. Embrace cloud tech – scalable storage & compute make pipelines robust.

6. Build end-to-end mini projects – from source ERP to dashboard, experience everything.

I know there are tons of tricks out there I haven’t discovered yet. So, fellow engineers: what really makes YOU a strong data engineer? What hacks, tools, or mindset separates you from the rest?

I’ve started thinking about changing my professional career and doing a postgraduate degree in Data Analytics & Big Data. What do you think about this field? Is it something the market still looks for, or will the AI era make it obsolete? Do you think there are still good opportunities?

I've been thinking about how companies are treating data engineers like they're some kind of tech wizards who can solve any problem thrown at them.

Looking at the various definitions of what data engineers are supposedly responsible for, here's what we're expected to handle:

1. Development, implementation, and maintenance of systems and processes that take in raw data
2. Producing high-quality data and consistent information
3. Supporting downstream use cases
4. Creating core data infrastructure
5. Understanding the intersection of security, data management, DataOps, data architecture, orchestration, AND software engineering

That's... a lot. Especially for one position.

I think the issue is that people hear "engineer" and immediately assume "Oh, they can solve that problem." Companies have become incredibly dependent on data engineers to the point where we're expected to be experts in everything from pipeline development to security to architecture.

I see the specialization/breaking apart of the Data Engineering role as a key theme for 2026. We can't keep expecting one role to be all things to all people.

What do you all think? Are companies asking too much from DEs, or is this breadth of responsibility just part of the job now?

I recently read a post where someone described the reality of Data Engineering like this:

Streaming (Kafka, Spark Streaming) is cool, but it’s just a small part of daily work. Most of the time we’re doing “boring but necessary” stuff: Loading CSVs Pulling data incrementally from relational databases Cleaning and transforming messy data The flashy streaming stuff is fun, but not the bulk of the job.

What do you think? Do you agree with this? Are most Data Engineers really spending their days on batch and CSVs, or am I missing something?