Speaker 1: Welcome to the deep dive, where we plunge into a
Speaker 1: stack of information, research notes, you name it, to really
Speaker 1: pull out those key nuggets of knowledge. We give you
Speaker 1: a serious shortcut to being well informed. Today, we're doing
Speaker 1: a deep dive into something I think is incredibly practical,
Speaker 1: really powerful for anyone you know, navigating the world a
Speaker 1: big data It's excerpts from the Azure Data Brooks cookbook
Speaker 1: Accelerate and Scale real Time Analytics. Think of this as
Speaker 1: your well, your essential guide to building solid, cutting edge
Speaker 1: data solutions on Azure. Our mission for you today is
Speaker 1: to really distill the core components of the key strategies
Speaker 1: from this cookbook so you grasp not just what adud
Speaker 1: data Brioks can do, but really how it's applied, you know,
Speaker 1: in the real world to tackle today's data challenges.
Speaker 2: Yeah, and what makes the source material so so compelling
Speaker 2: is really the background of the people involved, the authors,
Speaker 2: Fanie Raj and Vino Jazzwall. I mean, these are deeply
Speaker 2: experienced data architects engineers at Microsoft. We're talking over decade
Speaker 2: each and it specifically complex data warehouses, big data, real
Speaker 2: time solutions on as or it's their bread and butter
Speaker 2: and then you've got the reviewers on Kurna Or and
Speaker 2: Elan Bernardo Palasco. They add this whole other layer, you know,
Speaker 2: Advanced Data Architecture mL, scalable data pipeline. So this collective experience,
Speaker 2: it means what we're exploring isn't just theory, right, it's
Speaker 2: really grounded in hard one practical Know how you feel
Speaker 2: that reading it?
Speaker 1: Right? Okay, great point. So let's kick things off with
Speaker 1: the absolute basics, the fundamentals. If you're looking to get
Speaker 1: your hands dirty with Azure data bricks, the cookbook jumps
Speaker 1: right into creating the service, doesn't mess around. It walks
Speaker 1: you through setting up a workspace like directly in the
Speaker 1: Azure portal, and highlights these key decisions you make right
Speaker 1: at the start, like, for instance, the choice of vnet deployment.
Speaker 1: It shows selecting no initially maybe as a simpler start
Speaker 1: before you know review and create the service in your
Speaker 1: resource group like cookbook RG they use as an example.
Speaker 2: And that choice, the vnet deployment one. It's actually pretty significant,
Speaker 2: isn't it.
Speaker 1: Yeah.
Speaker 2: The book smartly brings up alternatives early, like using the
Speaker 2: Adurecli for deployment, and that's just crucial for automation, right,
Speaker 2: especially if you're thinking infrastructure as code, maybe scripting repeatable setups.
Speaker 2: We're using DevOps pipelines. Imagine like standing up a whole
Speaker 2: data bricks environment with just one command. That's the kind
Speaker 2: of power it hints at.
Speaker 1: Exactly. Okay, so you've got the workspace up and running,
Speaker 1: what's next? Access control? Obviously, the cookbook explains adding users
Speaker 1: groups straight from the Data Bricks admin console. Simple enough,
Speaker 1: but they need to be an as you're active directory first,
Speaker 1: that's the prerequisite. Then you get to the core, really
Speaker 1: creating and managing clusters. This is where the processing happens.
Speaker 1: You can spin them up from the UI, give it
Speaker 1: a name, pick a cluster mode like standard, that's the
Speaker 1: recommended one for single users, and it wisely defaults to
Speaker 1: terminating after what one hundred and twenty minutes of inactivity.
Speaker 2: Yeah, saves on costs, smart default, right.
Speaker 1: And you pick your Spark version data Bricks run time
Speaker 1: like Spark three point zero point one run time seven
Speaker 1: point four in their examples.
Speaker 3: And this discussion around cluster modes, that's where you really
Speaker 3: start tailoring things, optimizing your setup. So beyond that interact
Speaker 3: standard mode, the book introduces job clusters, and these aren't
Speaker 3: just like a minor variation, it's totally different approach for
Speaker 3: scheduled stuff. For automation, they spin up run your notebook.
Speaker 3: Job may be triggered by data factory. And then it's
Speaker 3: a cool part. They automatically delete them sales when done.
Speaker 3: So for you, that means well, potentially huge cost savings
Speaker 3: and super efficient resource use. You're only paying for compute
Speaker 3: when it's actually crunching numbers.
Speaker 1: Yeah, that auto delete is brilliant. And speaking of jobs,
Speaker 1: the cookbook makes that transition smooth from playing around a
Speaker 1: notebooks interactively to automating them. It shows uploading a notebook
Speaker 1: maybe a dot DBC file, running the cells, then scheduling
Speaker 1: it as a job in data bricks and here's that
Speaker 1: powerful bit again. You can configure the job to create
Speaker 1: a new on demand job cluster just for that one task,
Speaker 1: really flexible and then for anyone building integrations, you know,
Speaker 1: programmatically talking to data brooks. It covers authentication using Patsy's
Speaker 1: Personal Access tokens or Azure ad tokens to hit the
Speaker 1: rest APIs. It even shows connecting powerbi desktop using a
Speaker 1: pat to visualize data and spark tables like the MDA
Speaker 1: example they use. Okay, so environment's ready clusters are figured out.
Speaker 1: Now the big question how do you get your data
Speaker 1: in and out? The cookbook is really practical, step by
Speaker 1: step instructions for mounting storage, specifically ADLs GENT two as
Speaker 1: your data link storage gent too, and also as your
Speaker 1: blob storage, mounting them right into DDFS, the Data Bricks filesystem.
Speaker 1: It does involve registering an APP and AAD as your
Speaker 1: active directory to get those credentials application ID, tenant ID
Speaker 1: and the client's secret and obviously those secrets need super
Speaker 1: careful handling, store them securely.
Speaker 2: Absolutely, and this mounting process it's just such a game
Speaker 2: changer for making data accessible because it lets you treat
Speaker 2: your cloud storage almost like it's a local drive inside
Speaker 2: Data Bricks. It smoves out data access for your notebooks,
Speaker 2: makes interacting with potentially massive data sets feel well, seamless,
Speaker 2: much less clunky.
Speaker 1: Definitely, so storage is mounted now. The cookbook dives into
Speaker 1: reading and writing data different formats, different services that cover
Speaker 1: CSV and files in detail. You learned about Sparks schema inference.
Speaker 1: You know where it tries to guess the data.
Speaker 2: Types, which can be okay, but sometimes right.
Speaker 1: Sometimes it just sees everything as a string initially, so
Speaker 1: more importantly, it guides you through explicitly defining that schema
Speaker 1: using struct type, specifying things like integer type for a
Speaker 1: cus key column, making sure it's correct.
Speaker 2: And this is exactly where you unlock series performance gains
Speaker 2: that explicit schema definition plus the format choice itself. Park
Speaker 2: being columnar isn't just about compression, though that's nice. Its
Speaker 2: real power comes from optimizations like column pruning and predicate pushdown.
Speaker 2: Think about it, your query only needs two columns out
Speaker 2: of fifty, park let Spark read only those two columns,
Speaker 2: or if you have a filter or ware clause, it
Speaker 2: can push that filter down to the storage level. Avoids
Speaker 2: reading tons of irrelevant data compared to reading whole ROS
Speaker 2: and CSV or JSON. It's well, it's night and day
Speaker 2: for big data queries.
Speaker 1: Huge difference, and the cookbook doesn't stop there. It covers
Speaker 1: professing JSON too, even complexness. Did Jason shows you the
Speaker 1: Spark functions like toe json from Maryson and then beyond files,
Speaker 1: it talks about reading and writing to Azure sql database
Speaker 1: and also as your synaps analytics, specifically the dedicated seql
Speaker 1: pool using the native connectors.
Speaker 2: Yeah, and if you zoom out a bit, this ability
Speaker 2: to seamlessly integrate with all these services Azure, Sequel, synapps,
Speaker 2: even Cosmos dB which also have a Spark connector for
Speaker 2: batch and streaming. That's what really cements data bricks is
Speaker 2: this central hub, this sort of nervous system for a
Speaker 2: modern data platform. It's all about bringing together your diverse
Speaker 2: data sources into one place for analysis.
Speaker 1: Okay, let's peek under the hood a bit. I ever,
Speaker 1: wonder what Spark is actually doing when you run a query,
Speaker 1: it can feel like a black box. Sometimes this cookbook
Speaker 1: pulls back that curtain, introduces the concepts, jobs, stages, tasks,
Speaker 1: how Spark breaks.
Speaker 2: Down the work and the key visual here the really
Speaker 2: insightful bit is the directed acyclic graph the de gay.
Speaker 2: Think of it like Spark's internal blueprint for your query.
Speaker 2: It shows exactly how it plays to execute it and
Speaker 2: you can see this DAG in the SPARKUI. It breaks
Speaker 2: down your whole application into these jobs, stages and tasks.
Speaker 2: So for you the user, this is invaluable for debugging performance,
Speaker 2: Like if you see one task taking way longer than
Speaker 2: all the others, that's often your first big clue. You
Speaker 2: might have data skew where one partition has way more
Speaker 2: data than the others. The day helps you spot.
Speaker 1: That, and the book cleverly links this back to scheme
Speaker 1: definition shows how using that inferred schema we talked about
Speaker 1: it might lead to a more complicated dadgie more tasks,
Speaker 1: whereas providing an explicit schema upfront can simplify things, potentially
Speaker 1: cutting down execution time quite a bit. Open joins we
Speaker 1: all do joins. The cookbook explains how spucks optimizer is
Speaker 1: smart choosing between different algorithms like short merge or broadcast
Speaker 1: hash joins, but it also shows how you can influence
Speaker 1: that choice using hints in your seqlor data frame code
Speaker 1: to sugjust a specific joint strategy if you know something about.
Speaker 2: Your data, which leads to the million dollar question, how
Speaker 2: do you make your sparkax faster? The cookbook gets into
Speaker 2: the nitty gritty input partitions, shuffle partitions, output partitions. So
Speaker 2: Spark reads data from say EHDFS or ADLSM blocks. By default,
Speaker 2: each block might become one partition, but you can tweet
Speaker 2: settings like spark dot sqo, dot files dot max partition
Speaker 2: bites to control that initial partition size, which directly impacts parallelism.
Speaker 2: More smaller partitions can mean more tasks running in parallel.
Speaker 2: And then there's sparke dot sqol dot, shuffle dot partitions.
Speaker 2: Shuffling data between stages is expensive, involves network traffic. This
Speaker 2: setting controls how many partitions are created after a shuffle. Now,
Speaker 2: the book is honest, there's no single magic number for
Speaker 2: shuffle partitions. It really depends on your cluster size, your
Speaker 2: data volume. Begetting this reasonably right tuning it is absolutely
Speaker 2: critical for good performance. You have to experiment A.
Speaker 1: Bit makes sense, okay, shifting gears a bit real time data,
Speaker 1: it's everywhere now as your data bricks handles this With
Speaker 1: structured streaming, the cookbook gives good examples like reading streaming
Speaker 1: data from Kofka, or specifically kofka enabled event hubs in Azure,
Speaker 1: and even this clever trick treating a simple folder full
Speaker 1: of JSON log files as if it were a live
Speaker 1: streaming source, which is pretty neat.
Speaker 2: Yeah, that folder trick is handy, but one of the
Speaker 2: inherent challenges with any streaming system is late data right
Speaker 2: data arriving out of order. The cookbook points out how
Speaker 2: data brick structured streaming handles this pretty gracefully, automatically placing
Speaker 2: data into the correct time window. But this is where
Speaker 2: water marking comes in. It's a crucial concept. You essentially
Speaker 2: tell Spark, hey, data can be late, but only up
Speaker 2: to this much late. Anything older than the water markets ignored.
Speaker 2: This stops Spark from having to constantly update old aggregated
Speaker 2: results from ages ago, keeps things.
Speaker 1: Manageable right prevents infinite state, and the book details windowing
Speaker 1: for aggregations on streams explains both types. Tumbling windows those
Speaker 1: are fixed, non overlapping blocks of time like every five minutes,
Speaker 1: and then sliding windows. These overlap like a ten minute
Speaker 1: window that slides forward every five minutes. A single event
Speaker 1: can fall into multiple windows. It also clarifies offsets and checkpoints,
Speaker 1: especially for stateful streaming, where you're doing counts some averages
Speaker 1: over time. Spark processes the stream in microbatches. Checkpoints are
Speaker 1: how it remembers where it got up to the last
Speaker 1: offset processed in the source exactly.
Speaker 2: So if a job fails and restarts, the checkpoint lets
Speaker 2: it pick up right where it left off, ensuring no
Speaker 2: data is missed or processed twice it's key for fault
Speaker 2: tolerance and consistency.
Speaker 1: Okay, now this next part I think many people would
Speaker 1: agree this is where things get really interesting. Delta Lake.
Speaker 1: The cookbook presents this open source storage layer which sits
Speaker 1: right on top of your cloud storage like Adylus Gen two,
Speaker 1: and it positions Delta Lake as the solution, the answer
Speaker 1: to those classic data lake problems. No schema enforcement, no
Speaker 1: consistency guarantees, no acidy transactions, the data swamp problem.
Speaker 2: Oh. Absolutely, Delta Lake is a genuine game changer, bringing
Speaker 2: acid etymicity, consistency, isolation, durability, those database level guarantees, bringing
Speaker 2: them to the data lake. That's huge. Data lakes traditionally
Speaker 2: lack that, but Delta gives you reliable transactions plus scheme enforcement.
Speaker 2: Like you said, it rejects data that doesn't fit the
Speaker 2: table's structure, but it also allows scheme evolution, so you
Speaker 2: can change the schema over time as your data needs change.
Speaker 2: That's practical and crucially enabling it date and delete operations
Speaker 2: directly on your data lig files. That was a massive
Speaker 2: pain point before Delta Now it's straightforward.
Speaker 1: So the cookbook shows the basics naturally how to create
Speaker 1: Delta tables, read from them, write to them, saving data
Speaker 1: frames and Delta format, and it tackles concurrency, always a
Speaker 1: big issue in distributed systems. It explains how Delta uses
Speaker 1: optimistic concurrency control. Multiple jobs can try to write at
Speaker 1: the same time. Delta handles this by creating new table versions.
Speaker 1: If two jobs try to commit based on the same
Speaker 1: older version, only one succeeds, the other gets rejected. It
Speaker 1: even points out specific exceptions you might hit, like concurrent
Speaker 1: transaction exception or concurrent append exception, especially with multiple streaming
Speaker 1: queries hitting the same table right and the way.
Speaker 2: It handles this is pretty neat. It doesn't use traditional
Speaker 2: database locks, which can cause bottle. Instead, it makes sure
Speaker 2: a transactions trying to commit are processed mutually exclusively, one
Speaker 2: after the other. The first one wins, updates the transaction log,
Speaker 2: creates a new table version. The second one, seeing the
Speaker 2: table has changed underneath, it fails gracefully insurer's integrity and
Speaker 2: the book also notes that partitioning your Delta table smartly
Speaker 2: can really help reduce the chances of these conflicts in
Speaker 2: the first place.
Speaker 1: Good tip performance is always key to The cookbook introduces
Speaker 1: optimize and zorder optimize is about fixing the small file problem.
Speaker 1: It compacts lots of small data files into fewer, larger ones,
Speaker 1: much better for read performance, and zorder is even more advanced.
Speaker 1: It physically co locates related data within the files based
Speaker 1: on callers you specify exactly.
Speaker 2: It's like multi dimensional clustering. So when you queer with
Speaker 2: filters on those Z ordered columns, Spark can skip reading
Speaker 2: huge chunks of irrelevant data big speed up.
Speaker 1: Delta tables also support constraints like in databases. The cookbook
Speaker 1: mentions chie chick constraints evaluating a boolean expression for each
Speaker 1: row and standard not NLL constraints, and if you try
Speaker 1: to insert data that violates these you get an invariant
Speaker 1: violation exception helps maintain.
Speaker 2: Data quality, but honestly for you, the user, maybe one
Speaker 2: of the absolute coolest, most powerful features of Delta is
Speaker 2: the versioning and time travel. Every single change, every transaction
Speaker 2: is recorded in the Delta Transaction log. These Jason files
Speaker 2: in the Delta log folder. This means you have a
Speaker 2: complete history of your table. You could literally query the
Speaker 2: table as it was at a specific point in time
Speaker 2: or specific version number. Made a mistake, accidental delete, bad update,
Speaker 2: you can just query the previous version or even restore
Speaker 2: the table to that point. It's like a built in
Speaker 2: undue button for your entire data. Lake invaluable.
Speaker 1: That time travel is amazing. Okay, So the cookbook takes
Speaker 1: all these individual pieces, the setup, storage, spark, streaming, Delta
Speaker 1: and ties them together. It presents an end to end
Speaker 1: solution building near real time analytics and a modern data warehouse.
Speaker 1: It shows ingesting data from all sorts of places. Add
Speaker 1: your event hubs for the streaming stuff, Adlist two for
Speaker 1: batch files, maybe Azure sql database for lookup tables or metadata.
Speaker 2: Yeah, and the core architecture they showcase is very much
Speaker 2: that lake house pattern we hear about. It's powerful. The
Speaker 2: idea is you process all this diverse data, maybe land
Speaker 2: structured stuff in synapse analytics using traditional fact and dimension
Speaker 2: tables for BI, but you also keep the raw and
Speaker 2: processed data in delta lake Maybe it's some results in
Speaker 2: Cosmos dB two, specifically to power those near real time
Speaker 2: dashboards and applications. It blends the best of both worlds,
Speaker 2: the flexibility of a lake, the structure of a warehouse.
Speaker 1: Altho. The book walks through a scenario simulating vehicle sensor
Speaker 1: data Jason format streaming into event hubs, then Azure Data
Speaker 1: Bricks using Spark structured streaming picks it up, processes. It
Speaker 1: stores aggregated results in delta tables. Maybe the raw non
Speaker 1: aggregated data goes off to synaps and COSMOSDB as well.
Speaker 1: It shows processing both streaming and batch data together, even
Speaker 1: joining the live stream with static lookup tables pulled from
Speaker 1: Azure sql, and it explains the transformation stages using that
Speaker 1: medallion architecture bronze for raw silver, for cleaned enriched gold
Speaker 1: for aggregated business ready data, all typically stored as delta
Speaker 1: tables exactly.
Speaker 2: That bronze silk gold pattern is super common, provides.
Speaker 1: Great structure, and crucially, the cookbook shows you can build
Speaker 1: visualizations directly in a data bricks notebook for that near
Speaker 1: real time view, define queries, whip up bar charts, pie charts, whatever,
Speaker 1: and pin them to a notebook dashboard, and that dashboard
Speaker 1: can automatically refresh as new data streams in. Pretty cool
Speaker 1: for quick operational views, but for more robust enterprise bi
Speaker 1: it walks through connecting Powerbi using the native Azure Beta
Speaker 1: bricks connector in Powerbi desktop, you just need the server
Speaker 1: host name, HTTP path details from your data Bricks cluster,
Speaker 1: then you can directly query those Delta lake tables.
Speaker 2: So this direct connection is key because Data Bricks optimized
Speaker 2: engine working with Delta, combined with powerbi's native connector using
Speaker 2: efficient ODBC drivers, it means you can get really close
Speaker 2: to real time insights in your powerbi reports without constantly
Speaker 2: hidden refresh manually. It is designed for that low latency
Speaker 2: experience getting actionable intelligence fast.
Speaker 1: And finally, how do you automate this whole complex flow orchestration?
Speaker 1: The cookbook clearly shows using Azure Data Factory ADF adf
Speaker 1: acts as that serverlus et l e LT orchestrator, it
Speaker 1: can trigger your data Bricks notebooks, run other Azure tasks,
Speaker 1: manage dependencies, handle failures, basically run the entire end to
Speaker 1: end pipeline reliably. Okay, we're covering a lot, but no
Speaker 1: modern data solution discussion is complete without talking DevOps and
Speaker 1: security absolutely critical. The cookbook dedicates good sections to CICD
Speaker 1: continuous integration continuous deployment, specifically for your data Bricks notebooks
Speaker 1: using Azured DevOps.
Speaker 2: Yeah, and this is so important. It's not just about
Speaker 2: pushing code faster. It means proper source control for your notebooks.
Speaker 2: Maybe you can getthub or Azure repos versioning everything and
Speaker 2: then automating the deployment to different environments DEV test, UAT,
Speaker 2: PROD through release pipelines. It reduces manual effort, reduces errors,
Speaker 2: ensure you have consistent, reliable deployments every single time. It's
Speaker 2: professionalizing your data bricks.
Speaker 1: Development absolutely, and then security paramount the book details understanding
Speaker 1: and setting up RBAC role based access control and also
Speaker 1: ACL's access control lists within Azure. Specifically for your Adlsgen
Speaker 1: two storage. RBC lets you grant broader permissions like maybe
Speaker 1: storage blob data reader for a whole container or storage
Speaker 1: account right.
Speaker 2: RBAC is good for those broader strokes, but ACLS give
Speaker 2: you that really fine grain control. You can set read,
Speaker 2: write excute permissions on individual files and directories within the lake.
Speaker 2: This is essential if you have multiple teams sharing the
Speaker 2: lake or really sensitive data where you need to lock
Speaker 2: down access very tightly. You can grant access to specific
Speaker 2: addus or groups on specific folders, very granular.
Speaker 1: Another big security measure covered deploying data bricks itself into
Speaker 1: your own Azure virtual network of vnet. It explains provisioning
Speaker 1: data bricks workspaces within private and public subnets you control.
Speaker 1: This isolates your data bricks environment and lets you securely
Speaker 1: access things like Adlsgen two using private endpoints. Keeping traffic
Speaker 1: off the public Internet.
Speaker 2: And managing secrets always a headache. The integration with Azure
Speaker 2: key Vault is highlighted. Keyvolt becomes your central, super secure
Speaker 2: place to store things like storage account keys, database passwords,
Speaker 2: API keys. Your notebooks then fetch these secrets from keyvolt
Speaker 2: at runtime, rather than having them hard coded in the
Speaker 2: notebook itself, much much more secure. Similarly, azur app configuration
Speaker 2: is mentioned for managing application setting centrally keeping configurations separate
Speaker 2: from code. It can even reference secrets stored in key vault.
Speaker 1: And what about monitoring troubleshooting? The cookbook covers setting up
Speaker 1: a log analytics workspace and Azure Monitor and integrating data
Speaker 1: bricks to send its logs there sparklogs, cluster logs, audit logs.
Speaker 1: Then you can use KQL, the Custo query language to
Speaker 1: query all that telemetry data, find errors, track performance. You
Speaker 1: can even build dashboards in Azure Monitor to get a
Speaker 1: high level view of the health across all your Azure services,
Speaker 1: including Data Bricks, And lastly, within Data Bricks itself, there's
Speaker 1: cluster access control. Admins can define who is allowed to
Speaker 1: create clusters manage them. Plus cluster visibility control, especially in
Speaker 1: premium workspaces, restricts who can even see certain clusters, adds
Speaker 1: another layer of security and governance. Wow.
Speaker 4: Okay, that was a lot to unpack from just these excerpts,
Speaker 4: wasn't it. But hopefully you listening now have a really
Speaker 4: solid feel for the immense capabilities packed into Azure Data Bricks,
Speaker 4: especially for accelerating and scaling real time analytics. From just
Speaker 4: setting up the core services, handling all sorts of data collmats,
Speaker 4: optimizing Spark, dealing with streaming data, and then leveraging the
Speaker 4: frankly amazing power of Delta Lake.
Speaker 1: This cookbook really does lay out a comprehensive roadmap for
Speaker 1: anyone working with data on Azure today.
Speaker 5: Absolutely, and when you connect all those dots like we've
Speaker 5: tried to do, it's just clear that Data Bricks gives
Speaker 5: you this complete toolkit you can build genuinely robust modern
Speaker 5: data warehouses, near real time analytical solutions. You've got the
Speaker 5: visualization built in or connected via power BI. You've got
Speaker 5: the automation through ADF, and those critical security and DEVOFS
Speaker 5: integrations are cover. It really empowers you to build enterprise
Speaker 5: grade data platforms that can handle pretty much anything you
Speaker 5: throw at them.
Speaker 1: So what does this all mean for you? Practically well,
Speaker 1: with these kinds of tools at your fingertips managing complex,
Speaker 1: large scale data systems on Azure, It's not just possible,
Speaker 1: it's highly optimized. It lets you move beyond just old
Speaker 1: school batch processing and really embrace real time insights, get
Speaker 1: answers faster, all while making sure your data is reliable, consistent,
Speaker 1: and secure thanks to things like Delta Lake and the
Speaker 1: security features. So as you think about the sheer volume
Speaker 1: and speed of data being generated today, here's maybe a
Speaker 1: final thought for you to moll over. If Delta Lake
Speaker 1: can bring those database like guarantees, ase transactions, scheme enforcement
Speaker 1: to the inherent flexibility and scale of a data lake,
Speaker 1: does this fundamentally change how we should think about designing
Speaker 1: all our future data architectures. Does it push us firmly
Speaker 1: into that lakehouse paradigm as the default for almost every
Speaker 1: kind of data? And what new possibilities does that unlock?
Speaker 1: For your next big data challenge.