I am a data engineer, and I STILL don't understand the differences between the following terms:
1. Data Warehouse
2. Datalake
3. Data Lakehouse
4. Data Mesh
Can someone please clearly explain the differences between these concepts?
1. Data Warehouse
2. Datalake
3. Data Lakehouse
4. Data Mesh
Can someone please clearly explain the differences between these concepts?
Data Warehouse. SQL engine that is much bigger than the databases of yester-year. Their size and scalability give you capabilities that DBAs of a MS SQL dB wouldn't necessarily allow you to do, but think "wow, that is a big database!"
Data Lake. Now your database is stored in blob storage (which is like CSV files on your computer) - and the blob storage is HUGE (like hundreds of Terabytes or larger). Also, you store unstructured data, too. So: pictures, JSONs, raw HTML. Whatever
Data Lakehouse... do you mean Delta Lake? This is a made up word from Dara Bricks. Take your Data lake, and slap a SQL engine on top of it, and add in some marketing slang ;)
Data Mesh. THIS is more about organizations than infrastructure. Imagine a Data Warehouse or Datalake. Now find 5 key stakeholders around your company. Let each of them have a playground in your warehouse. You drop the data into one zone, then they copy it, transform it, and share it in their zone - but they have to play by your rules. Bam. Data Mesh
Delta files are actually an amazing format though. At their most basic, they took Parquet files (columnar database) and let you stream off them really easily. Which takes a lot of complexity out of your pipelines - dont need Kafka for everything, don’t need to figure out when new rows get added (or a whole other set of jobs around that).
But using Delta files really can change the way you develop pipelines (and ML pipelines), so I forgive them for inventing a new term.
I am however keeping up to date with Apache Iceberg bc it's much easier to follow and it seems to have a lot of advantages over the Delta Lake external table format (delta files?).
Iceberg seems to be better especially in handling schema evolution and drift. They both seem to use parquet and avro below the surface and generally have the same design, but am I missing anything by dismissing and ignoring Delta Lake?
With that out of the way, the Data Lakehouse name has a Marketing motivation, but the concept is “let’s not have a DW and a Lake as separate structures based on different technical stacks - let’s have one stack that can do both”. That is a very valid proposition, because the information needs tend to span what a DW or a Lake can easily do by themselves. Also, having one common stack makes it much easier to manage all data and generally be productive.
If you look back even at Inmon’s Corporate Information Factory, you’ll see mentions to unstructured data coming in, and the expectation that all data, structure or unstructured, should become useful information for some business purpose. This was back in the 90s, the need was already there, but the technology was not.
The Lakehouse as proposed by Databricks is essentially a combination of technologies (including but not limited to Delta Lake format) to deliver that unified design. It’s a platform where you can process all kinds of data, apply structuring where needed, manage quality, do DataOps and generally deliver information, regardless of the structure or lack of it in the original data.
So, to put it simply, a Lakehouse is a platform (or technical stack, if you want to build your own) where you can implement a DW, a Data Lake and mix capabilities of both to deliver data products more efficiently. It’s more than just the union of DW and Lake, because the combined capabilities allow you to do things that neither could by itself. For instance, parse a large set of documents for sentiment and context to generate metrics on some business topic. Environment, Sustainability and Governance - ESG - is one example area where the results are BI-like metrics for reports and dashboards, but the sources are usually text documents. Social media processing for Marketing purposes is another.
Warehouse and Lake also feel mostly like distinctions without difference, though if you really want, I think this is the technical difference: - warehouse: structured data for analytics, outputted from ETL jobs - lake: a dump for unstructured data, to be cleaned up later: EL, then possibly T
Moreover, people want to push this even further, with "data river" - streaming(-like) data that is "continuously" transformed (isn't that just an ETL data pipeline?)
Recommend reading up on Kimball vs Inmon.
Kimball -- centralised storage in a "dimensional model". Uses a star/snowflake schema where the central FACT table (containing the raw transactional data) can be filtered and aggregated based on choices made in dimension tables (edges of the star/snowflake).
demographic account_types product_types
\ / /
\ / /
customer product
\ /
\ /
FACT table (sale $, order quantity, order ID, product ID)
The FACT table makes "measures" available to the user. Example: Count of Orders. These are based on the values in the FACT table (your big table of IDs that link to dimensions and low-level transactional data).
You can then slice and dice your count of orders by fields in the dimensions.
You could then add Sum of Sale ($) as an additional measure. "Abstract" measures like Average Sale ($) per Order can also be added in the OLTP backend engine.
End users will often be using Excel or Tableau to create their own dashboards / graphs / reports. This pattern makes sense in that case --> user can explore the heavily structured business data according to all the pre-existing business rules.
Pros:
- Great for enterprise businesses with existing application databases
- Highly structured and transaction support (ACID compliance)
- Ease of use for end business user (create a new pivot table in Excel)
- Easy to query (basically a bunch of SQL queries)
- Encapsulates all your business rules in one place -- a.k.a. single source of truth.
Cons
- Massive start up cost (have to work out the schema before you even write any code)
- Slow to change (imagine if the raw transaction amounts suddenly changed to £ after a certain date!)
- Massive nightly ETL jobs (these break fairly often)
- Usually proprietary tooling / storage (think MS SQL Server)
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2. Data Lake
Throw everything into an S3 bucket. Database table? Throw it into the S3 bucket. Image data? Throw it into the S3 bucket. Kitchen sink? Throw it into the S3 bucket.
Process your data when you're ready to process it. Read in your data from S3, process it, write back to S3 as an "output file" for downstream consumption.
Pros:
- Easy to set up
- Fairly simple and standardised i/o (S3 apis work with pandas and pyspark dataframes etc)
- Can store data remotely until ready to process it
- Highly flexible as mostly unstructured (create new S3 keys -- a.k.a. directories -- on the fly )
- Cheap storage
Cons:
- Doesn't scale -- turns into a "data swamp"
- Not always ACID compliant (looking at you Digital Ocean)
- Very easy to duplicate data
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3. Data Lakehouse
Essentially a data lake with some additional bits.
EDIT: aims to mitigate against the "data swamp" problem, improve flexibility / performance when changing how some-random-column X is calculated, improved governance and auditability.
A. Delta Lake Storage Format a.k.a. Delta Tables
EDIT: apparently there are a few more players and not just delta lake (Apache iceberg). But there’s still questions around how open the open source for these things are.
Versioned files acting like versioned tables. Writing to a file will create a new version of the file, with previous versions stored for a set number of updates. Appending to the file creates a new version of the file in the same way (e.g. add a new order streamed in from the ordering application).
Every file -- a.k.a. delta table -- becomes ACID compliant. You can rollback the table to last week and replay e.g. because change X caused bug Y to happen.
AWS does allow you do this, but it was a right ol' pain in the arse whenever I had to deal with massively partitioned parquet files. Delta Lake makes versioning the outputs much easier and it is much easier to rollback.
B. Data Storage Layout
Enforce a schema based on processing stages to get some performance & data governance benefits.
Example processing stage schema: DATA IN -> EXTRACT -> TRANSFORM -> AGGREGATE -> REPORTABLE
Or the "medallion" schema: Bronze -> Silver -> Gold.
Write out the data at each processing stage to a delta lake table/file. You can now query 5x data sources instead of 2x. The table's rarity indicates the degree of "data enrichment" you have performed -- i.e. how useful have you made the data. Want to update the codebase for the AGGREGATE stage? Just rerun from the TRANSFORM table (rather than run it all from scratch). This also acts as a caching layer. In a Data Warehouse, the entire query needs to be run from scratch each time you change a field. Here, you could just deliver the REPORTABLE tables as artefacts whenever you change them.
C. "Metadata" Tracking
See AWS Glue Data Catalog.
Index files that match a specific S3 key pattern and/or file format and/or AWS S3 tag etc. throughout your S3 bucket. Store the results in a publicly accessible table. Now you can perform SQL queries against the metadata of your data. Want to find that file you were working on last week? Run a query based on last modified time. Want to find files that contain a specific column name? Run a query based on column names.
EDIT: also helps with data governance and auditability as all of your files/tables are tracked in a centralised location.
Pros:
- transactional versioning -- ACID compliance and the ability to rollback data over time (I accidentally deleted an entire column of data / calculated VAT wrong yesterday)
- processing-stage schema storage layout acts as a caching layer (only process from the stage where you need to)
- no need for humans to remember the specific path to the files they were working on as files are all indexed
- less chance of creating a "data swamp"
- changes become easier to audit as you can track the changes between versions
Cons:
- Delta lake table format is only really available with Apache Spark / Databricks processing engines (mostly, for now)
- Requires enforcement of the processing-stage schema (your data scientists will just ignore you when you request they start using it)
- More setup cost than a simple data lake
- Basically a move back towards proprietary tooling (some FOSS libs are starting to pop up for delta tables, plus git-based data versioning is a thing)
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4. Data Mesh
geoduck14's answer on this was pretty good. basically have a data infrastructure team, and them domain-specific teams that spring up as needed (like an infra team looking after your k8s clusters, and application teams that use the clusters). domain specific data team use the data platform provided by the data infrastructure team.
Previously worked somewhere in a "product" team which basically performed this function. They just didn't call it a "data mesh".
The goal for a Lakehouse is to create a platform/stack that can effectively do everything a DW does, everything a Data Lake does (so, all your pros above), get rid of most of the cons and also allow mixing and matching of the functionalities. Acknowledging this need to handle all kinds of data is not new; having the technology to deliver on it is, and that’s what the Lakehouse is.
I was on the fence about including mentions of querying mechanisms like AWS Athena and the like, but you’re right it’s all part of the same horse (or house).
Was 5am by the time I finished writing this though… so I kind of had to stop (hence the very short data mesh section!).