The Databricks Data Engineer

Jakub Lasak

Helping 18k+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors.

  1. The Databricks pipeline that ran green for a year and was wrong the whole time

    2 દિવસ પહેલાં

    The Databricks pipeline that ran green for a year and was wrong the whole time

    A revenue-attribution pipeline runs overnight, bronze to silver to gold, and lands a clean set of numbers the whole company uses to decide where the money goes. Every morning: green. Every morning, everyone moves on with their day. For a year, nobody looked any closer. The job succeeded every single night, so everyone trusted the data. But "finished" and "correct" are two different words, and the space between them had been quietly swallowing a slice of revenue for eleven months, with no error, no warning, and not one alert. In this episode: - Why a Databricks job can run green every day and still feed wrong numbers into decisions that matter - The one question that separates "did it run" from "is it right" and cracks silent failures wide open - How an ordinary upstream schema change and an inner join can drop rows for months without tripping anything - The single correctness check that would have caught this on night one instead of month eleven - What to say in the room after an incident like this if you want leadership to trust you with the next hard problem This episode is for Databricks data engineers who own pipelines whose numbers someone actually makes decisions on. Whether you've ever said "the pipeline's fine, it's probably the dashboard" or you're staring at an all-green run history right now, you'll walk away knowing exactly what to check before you trust it again. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    8 મિ૰
  2. How Photon actually makes your Databricks queries faster (and when it silently doesn't)

    29 જૂન

    How Photon actually makes your Databricks queries faster (and when it silently doesn't)

    Two engineers run the same SQL on the same Delta table. Same data, same cluster size, copy-pasted code. Alex goes to make a coffee and comes back to a query still running. Sam's is done before they finish reading the first Slack message. The only difference is one checkbox on the cluster called Photon. Most Databricks data engineers have that box ticked, pay a premium for it on every DBU, and could not explain in plain English what it actually does. "Photon makes it faster" isn't an answer - it's just saying the box does what the box does. And there's one thing it quietly stops doing while you keep paying for it. In this episode: - The single architectural change underneath Photon, explained without one line of code or config - A mental model that lets you explain Photon to a PM in 60 seconds flat - Why Photon flies on scans, filters, joins, and aggregations but does nothing for certain custom code - The silent fallback that charges you the fast-engine premium for slow-engine work, with nothing on screen to warn you - The one query to open in the query profile this week, and the single question to ask of it This episode is for Databricks data engineers who run Photon every day and treat it as a speed toggle they never think about. Whether you're a mid-level engineer who clicks the box and moves on, or a senior who's never checked whether the premium is paying off, you'll walk away knowing exactly how to tell when Photon is doing the work you're being billed for - and when it isn't. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    11 મિ૰
  3. The Databricks interview round nobody studies for (and almost everybody fails)

    22 જૂન

    The Databricks interview round nobody studies for (and almost everybody fails)

    Picture the debrief room after a Databricks loop. Two candidates went through that day. On paper, a coin flip: SQL tied, Spark internals solid, system design clean for both. Score only the rounds with a rubric and you cannot separate them. And yet the room isn't split. One gets the offer, and the thing that decided it wasn't any of the rounds they studied for. It was the conversation everyone treats as filler. There's a reason your strongest technical answers can't win it for you, and it's not the one you'd guess. In this episode: - Why the round with no whiteboard and no visible rubric is the one that decides close calls - What it actually measures, and why your best technical round can't test it - The three ways strong engineers fail it without ever noticing - Why "tell me about a decision you'd make differently" is a trap baited with your own best work - The one prep move that needs zero new facts, just a few honest walks This episode is for Databricks data engineers cleared past the technical bar who keep losing the close calls. Whether you're prepping for your next loop or wondering why a strong interview still ended in a no, you'll walk away with a specific way to rehearse the conversation everyone treats as a throwaway. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    12 મિ૰
  4. The Spark Shuffle is baggage claim: why your job waits instead of computes (and more workers won't fix it)

    15 જૂન

    The Spark Shuffle is baggage claim: why your job waits instead of computes (and more workers won't fix it)

    Your Spark job has been running for forty minutes. The dashboard shows your cluster isn't even busy. So you do the obvious thing: add more workers. And it changes nothing. Here's why. During a shuffle, Spark is barely computing at all. It's tagging every row by destination, piling rows together, spilling the overflow to disk, and hauling data across the network between executors. It's an airport rerouting every passenger's bag to a new carousel, and more baggage handlers can't speed up a single overloaded belt. In this episode: - Why your slowest wide transformation spends most of its time on logistics, not computing - The four-step model that lets you explain the shuffle to a teammate in sixty seconds - Why adding workers can make a skewed job slower, not faster - The two numbers in the Spark UI that tell you whether it's skew, partition count, or spill - The one diagnostic to run before you ever resize the cluster again This episode is for Databricks data engineers whose joins and aggregations crawl for reasons the cluster size never seems to fix. Whether you're mid-level and tired of guessing, or senior and tired of paying for compute that doesn't help, you'll walk away able to read a slow shuffle instead of throwing hardware at it. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    11 મિ૰
  5. Your Databricks data quality framework is a Yeti: everyone talks about it, nobody has seen it work

    8 જૂન

    Your Databricks data quality framework is a Yeti: everyone talks about it, nobody has seen it work

    An architecture review. A platform team is presenting their data quality setup, and honestly, it's impressive. Expectations on every ingestion table. Drift metrics on the dashboard. A dedicated alerts channel. Then a finance engineer asks the only question that counts: when did this last catch something before one of us did? Silence. That silence is the whole problem. The decks, the suites, the dashboards are everywhere. The proof that any of it actually works is somewhere else entirely. In this episode: - Why the discipline every Databricks team talks about is the one with the fewest confirmed wins - A 30-second test that tells you if your data quality framework is alive or just well documented - Why checks chosen by what's easy to write miss the incidents that actually break you - The one deadline-night decision that quietly kills more frameworks than any outage - The reframe that separates teams who get caught off guard from teams who don't This episode is for Databricks data engineers who own pipelines feeding the numbers their company argues about. Whether you've got expectations in warn mode you never reverted, or a monitoring dashboard nobody reads, you'll walk away with a way to tell whether your framework is real and what to do if it isn't. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #DataQuality #ApacheSpark #DeltaLake

    12 મિ૰
  6. Why senior Databricks engineers write less code than mid-level ones

    2 જૂન

    Why senior Databricks engineers write less code than mid-level ones

    Two engineers, same team, both five years in. Last quarter Mark shipped forty-seven pull requests across three pipelines. Sam shipped nine. On any dashboard, Mark wins by a mile. Sam got the staff offer. Mark got a kind note about continuing to demonstrate impact. This isn't politics, and it isn't luck. It's a pattern that specifically catches the engineers who are best at shipping, because the most valuable work a senior Databricks data engineer does is invisible by construction. You can't put a ticket number on a problem that never happened. In this episode: - Why the exact behavior that makes you great at mid-level is the behavior that keeps you stuck there - The three categories of senior work that produce zero lines of code but move the entire platform - How to tell leveraged work apart from work that just feels safe to ship - Why "less code" is a symptom and not a goal, and the failure mode of engineers who get that backwards - The one question to ask before your hands hit the keyboard that changes what you volunteer for next sprint This episode is for Databricks data engineers who ship more than anyone on the team and quietly wonder why it isn't landing at review time. Whether you're a mid-level engineer optimizing the wrong line on the chart, or a senior tired of watching your highest-leverage work go uncounted, you'll walk away with language to make prevented work legible and a lens for spending your hours where they actually compound. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    11 મિ૰
  7. 4 habits that quietly turn your Databricks Delta Lake into a swamp

    26 મે

    4 habits that quietly turn your Databricks Delta Lake into a swamp

    You built the table right. Well-partitioned, documented, fast enough that the row count came back before you finished reading your own Slack. Six months later it takes four minutes to return that same count, and nobody on your team ever decided to make it that way. There was no meeting, no design doc, no ticket titled "let's make this unqueryable by Q3." A swamp is not a decision. It's the sum of a few dozen reasonable shortcuts that compound into something nobody would have signed off on if you'd proposed it all at once. Which is why telling people to "be more careful" never fixes it. They were already careful. In this episode: - Why your slowest Delta table isn't slow because the data is big, and what it's actually choking on - The storage-bill surprise that's invisible in every query until the invoice lands - How the most generous thing you do for a blocked teammate quietly destroys whether anyone can trust the table - Why nobody can clean up a swamp where nobody knows what's load-bearing, and the cheapest fix in the whole estate - When you should ignore all of this advice, because over-governing a throwaway table is just a different swamp This episode is for Databricks data engineers staring at the one table everyone groans about, the one that actually matters, wondering how it got like this. Whether you run batch, streaming, or DLT, you'll walk away able to name exactly which kind of rot is filling your worst table and the specific senior counter-move that reverses it. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    12 મિ૰
  8. Liquid Clustering vs Z-Ordering: 4 questions that decide

    18 મે

    Liquid Clustering vs Z-Ordering: 4 questions that decide

    You open your Databricks workspace. Two Delta tables. Same size, same downstream BI workload. Table A was partitioned and z-ordered in 2023, runs fine. Table B is greenfield this quarter, liquid clustering by default. Your tech lead asks how aggressive you want to be with migration tickets. Whatever you type back is probably wrong. This is not a feature swap. It's a paradigm shift, and the migration math only makes sense once you can name what actually moved underneath you. Migrate-everything is wrong. Migrate-nothing is wrong. The right answer is per-table, with named criteria. In this episode: - What actually changed when liquid clustering shipped, and the one phrase that simplifies every migration debate you'll have for the next two years - The four-question filter to run table by table, in order, before you commit to a layout decision - The surviving cases where the old paradigm still wins, including the one the evangelism crowd never names - Why liquid clustering and partitioning on a Delta table are mutually exclusive, and the operational property you give up if you migrate the wrong tables - The named audit that turns six hundred legacy tables into three buckets in an afternoon - What kind of senior engineer your tech lead remembers when the promotion conversation happens This episode is for Databricks data engineers staring at a migration backlog, defending a greenfield default, or trying to explain to a platform team why some tables shouldn't be touched. Whether you're a mid-level engineer running your first migration, or a senior engineer setting the standard for the next two years of greenfield Delta tables, you'll walk away with a defended per-table answer and the vocabulary to back it up. --- Helping 18,000+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors. Follow The Databricks Data Engineer for new episodes every Monday, Wednesday, and Friday. LinkedIn: linkedin.com/in/jrlasak Newsletter: dataengineer.wiki #DataEngineering #Databricks #DataEngineer #CareerGrowth #ApacheSpark #DeltaLake

    18 મિ૰
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Helping 18k+ Databricks data engineers become seniors: interview like seniors, execute like seniors, think like seniors.

માહિતી

  • નિર્માતા
    Jakub Lasak
  • વર્ષ સક્રિય
    2 હજાર
  • ઍપિસોડ
    15
  • રેટિંગ
    સાફસૂથરું
  • કૉપિરાઇટ
    © Jakub Lasak
  • શોની વેબસાઇટ
    The Databricks Data Engineer

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