MTA vs. MMM: Which Marketing Measurement Model is Right for You?
MTA vs. MMM: Understand the differences, strengths, and weaknesses of each marketing measurement model to choose the right one for your business.
Nitin Mahajan
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December 29, 2025
So, you've probably heard about Parquet files, especially if you're working with big data. It's this file format that pops up everywhere, from data lakes to analytics tools. But what exactly is Parquet file format, and why is it such a big deal? Think of it like a super-organized way to store data, but instead of putting everything in rows like a spreadsheet, it stores data column by column. This might sound simple, but it makes a huge difference when you're dealing with massive amounts of information and need to get insights quickly. Let's break down what makes Parquet tick.
So, what exactly is this Parquet thing everyone's talking about in the data world? Think of it as a special way to store data, especially when you've got a ton of it. Instead of storing data row by row, like you might in a simple spreadsheet, Parquet stores it column by column. This might sound like a small change, but it makes a huge difference for how quickly you can get information out, particularly for analytical tasks. It's become a big deal in big data because it's just so good at handling large datasets efficiently. You'll find it used all over the place in modern data pipelines and data lakes.
At its heart, Parquet is all about columnar storage. Imagine you have a table with columns like 'Name', 'Age', and 'City'. In a traditional row-based format, all the data for 'Alice' (her name, age, and city) would be stored together, then all the data for 'Bob', and so on. With columnar storage, all the 'Name' data is stored together, then all the 'Age' data, and then all the 'City' data. This has some pretty neat advantages:
This column-oriented approach means that when you query specific columns, you're only reading the data you actually need. It's like only pulling out the ingredients you need for a recipe instead of bringing the whole pantry to your counter.
Parquet wasn't just invented out of thin air. It was heavily inspired by research, particularly from Google's Dremel system, which was designed for fast data analysis. The goal was to create a file format that could handle massive datasets and be super efficient for querying. Early on, it was adopted by big data tools like Apache Hive and Pig, and its popularity just grew from there. It's now a standard in many data lakes and processing frameworks like Spark and Flink.
Parquet has become a cornerstone in the big data world. Why? Because it plays nicely with so many different tools and systems. It's often the default or recommended format for data lakes built on platforms like Hadoop, cloud storage (like S3 or ADLS), and data warehousing solutions. Its efficiency in both storage and query performance makes it a go-to for anyone dealing with large-scale data processing and analytics. It's a format that's designed to scale.
Alright, so we've talked about what Parquet is and why it's cool for big data. Now, let's get into the nitty-gritty of how these files are actually put together. Think of it like building with LEGOs; Parquet has its own way of stacking and organizing pieces to make things work efficiently.
Parquet doesn't just dump all your data in one giant block. Instead, it breaks it down into what it calls "row groups." These are basically chunks of your data, where all the columns for a specific set of rows are kept together. Imagine you have a table with a million rows; a row group might hold, say, 10,000 of those rows. This grouping is super helpful because if you're looking for data within a certain range of rows, you might only need to look at a few row groups instead of the whole file. Plus, each row group keeps track of things like the minimum and maximum values for each column within that group. This means if your query asks for ages between 30 and 40, and a row group's Age column only has values from 10 to 20, the system can just skip that whole group without even looking inside. Pretty neat, right?
Now, within each row group, Parquet gets even more specific. Instead of storing all the data for a row together (like in a traditional row-based format), it stores data column by column. So, for a given row group, you'll have a "column chunk" for ID, another for Name, another for Age, and so on. This is where the real magic for analytics happens. If your query only needs the Age column, the system can just grab the Age column chunks from the relevant row groups and completely ignore the ID and Name chunks. This drastically cuts down on the amount of data that needs to be read from disk, which is usually the slowest part of any data operation.
Okay, so we have row groups, and within those, we have column chunks. But Parquet doesn't stop there. Each column chunk is further divided into "pages." These are the smallest units of data in a Parquet file. A page typically holds data for a single column within a row group. These pages can contain the actual data values, or they might contain metadata like a dictionary for encoding repeated values (we'll get to that later). The size of these pages is configurable, but they're generally kept small enough to be decompressed and processed efficiently in memory. This granular structure allows for very fine-tuned reading and processing of data.
The way Parquet structures its files, from row groups down to pages, is all about making data access faster, especially for analytical queries that often only need a fraction of the total data. It's a carefully designed system to minimize I/O and maximize processing speed.
Here's a simplified look at how it all fits together:
Parquet File | +-- Row Group 1 | | | +-- Column Chunk (Column A) | | +-- Page 1 | | +-- Page 2 | | | +-- Column Chunk (Column B) | | +-- Page 1 | | | +-- Row Group 2 | +-- Column Chunk (Column A) | +-- Page 1 | +-- Column Chunk (Column B) +-- Page 1When you're working with Parquet, how the data is actually packed and squeezed down makes a big difference. It's not just about shoving data into a file; it's about being smart with it. Parquet uses a couple of main tricks: encoding and compression. Think of encoding as organizing the data within a column so it's easier to compress, and compression as the actual squishing process to make the file smaller.
Lots of datasets have columns where the same values pop up over and over. Think of a 'country' column or a 'status' field. Instead of writing 'United States' a thousand times, dictionary encoding replaces each unique value with a small integer ID. So, 'United States' might become '1', 'Canada' might be '2', and so on. The file then stores a small dictionary mapping these IDs back to the original values, plus a list of these IDs. This can seriously shrink file sizes, especially when combined with compression.
Parquet lets you pick from a few different ways to compress your data. Each has its own trade-offs between how much it shrinks the file and how fast it is to compress and decompress.
Here's a quick rundown:
Choosing the right algorithm really depends on what you need. If you're running lots of interactive queries, Snappy or ZSTD might be best. If you're just storing data for the long haul and want to save on storage costs, Gzip or Brotli could be the way to go.
The decision between compression algorithms isn't just about picking the smallest file size. You have to think about how quickly you need to read that data back. Sometimes, a slightly larger file that decompresses almost instantly is way better than a tiny file that takes ages to open.
Parquet handles complex, nested data structures (like lists within lists, or objects inside objects) really well. It uses two clever mechanisms: repetition levels and definition levels. These are like little instructions that tell Parquet how to reconstruct the nested structure from the flattened, column-based data. Repetition levels track how many times a repeated element (like an item in a list) has been repeated at a certain level of nesting. Definition levels, on the other hand, indicate whether a particular value is present or if it's null, especially important for optional fields in nested structures. Together, they allow Parquet to efficiently store and retrieve intricate data without needing to duplicate a lot of information.
So, you've got your data and you're ready to save it in Parquet format. It's not just a simple save operation; there are a few steps involved to make sure it's done right. Think of it like packing a suitcase for a long trip – you want to organize things efficiently so you can find what you need later.
First things first, you need to tell Parquet what your data looks like. This is the schema. It's basically a blueprint that defines the names of your columns and what type of data goes into each one – like numbers, text, dates, or even more complex structures. Getting this right upfront is pretty important because Parquet files are immutable, meaning you can't easily change the schema after the fact. Libraries like PyArrow make defining these schemas straightforward.
Here's a quick look at how you might define a simple schema:
UserId: An integer (like 1, 2, 3).Name: Text (like 'Alice', 'Bob').Age: Another integer, but maybe a smaller one than UserId.Once the schema is set, the data itself needs to be organized. Parquet doesn't just dump everything in one big block. It breaks data down into "row groups." Each row group holds a chunk of your rows, but importantly, it stores the data for each column separately within that group. This is the "columnar" part. So, instead of having all the data for row 1, then all for row 2, you have all the UserIds together, all the Names together, and so on, within a row group. These column segments are called "column chunks."
This organization is key for performance later on. If you only need to read the Name column, the system can skip reading the Age or UserId data for that row group.
Finally, all this organized data, along with the schema information and statistics about the data (like the minimum and maximum values in a column chunk), gets written to disk. This collection of information is what makes up the Parquet file. The process usually involves:
The way data is structured into row groups and column chunks, along with the encoding and compression choices, directly impacts how quickly you can read specific parts of the file later. It's all about making those read-heavy analytical queries faster by minimizing the amount of data that needs to be pulled from storage.
So, you've got this Parquet file, maybe from a data lake or a big data job, and now you need to actually get some information out of it. It's not like a CSV where you can just open it in a text editor and squint at the data. Parquet is a bit more structured, which is good for performance, but it means you need the right tools to read it.
Before you even start pulling data, it's super helpful to know what's inside. Parquet files have this metadata section that acts like a table of contents. It tells you about the schema – the names of the columns, their data types, and how they're organized. It also contains information about the row groups and column chunks, like the number of rows in each group and the min/max values for columns within those chunks. This is key for optimizing your queries because it lets the reading tool skip over data it doesn't need.
This is where the columnar nature really shines. When you run a query, say you only need a couple of columns and maybe only data from the last month, the Parquet reader can be really smart about it. It looks at that metadata we just talked about. If your data is split into row groups, and the metadata tells it that the last month's data is all in, say, row groups 5 through 10, it'll just read those. Then, within those row groups, if you only asked for user_id and purchase_amount, it'll only pull those specific column chunks. It doesn't have to read the whole file, or even whole rows, just the bits it needs. This is a massive speed-up compared to row-based formats.
Once the reader has grabbed the necessary column chunks, they're usually compressed. So, the next step is to decompress them. Parquet supports various compression methods like Snappy, Gzip, or LZO, and the reader needs to know which one was used to unpack the data correctly. After decompression, the data is still in its columnar chunks. The final step is to 'materialize' this data, which basically means reconstructing the rows so you can work with them, often by converting them into a format like a Pandas DataFrame in Python or a similar structure in other tools. This process turns those compressed, column-specific bits back into a usable table.
The efficiency of reading Parquet files hinges on the reader's ability to interpret the file's metadata. This allows for targeted data retrieval, skipping unnecessary row groups and columns, and then efficiently decompressing and assembling only the required data. This selective access is the primary driver behind Parquet's performance gains for analytical workloads.
So, why has Parquet become such a big deal in the data world? It really boils down to a few key strengths, but like anything, it's not perfect for every single situation.
Parquet's columnar structure is a game-changer for analytical queries. Instead of reading an entire row when you only need a couple of columns, Parquet lets you just grab the specific columns you're interested in. Think about a massive table with hundreds of columns – if your query only needs three, you're saving a ton of work by not pulling all that other data. This means faster query times, especially when you're sifting through huge datasets looking for specific insights. It's like only grabbing the ingredients you need from the pantry instead of hauling out the whole shelf.
Because Parquet stores data column by column, all the values within a single column are of the same data type and often have similar patterns. This makes them ripe for compression. Algorithms like Snappy, Gzip, and LZO can work their magic much more effectively on homogeneous data. This means your data takes up less space on disk, which translates directly into lower storage costs. Plus, less data to read from disk also speeds up those analytical queries we just talked about.
Here's a quick look at how compression can impact file size:
Note: Ratios and speeds are approximate and depend heavily on the data itself.
While Parquet shines for analytics, it's not always the best fit. If you're dealing with very small, transactional datasets where you're constantly reading and writing individual rows, the overhead of Parquet's structure (like row groups and metadata) can actually slow things down. It's designed for bulk operations and analytical workloads. Also, Parquet is a binary format, meaning you can't just open a .parquet file in a text editor and read it like a CSV. This makes direct human inspection a bit trickier.
Parquet's design prioritizes read performance for analytical tasks and storage efficiency. This focus means that for workloads involving frequent, small writes or row-level lookups, other formats might offer a simpler and more performant solution. Understanding your primary use case is key to deciding if Parquet is the right choice.
So, that's the lowdown on Parquet files. We've looked at how they're put together, why they're so good at storing big chunks of data efficiently, and how tools use that structure to speed things up. It's not just some tech buzzword; it's a practical way to handle data that makes a real difference when you're dealing with large amounts. Whether you're working with data lakes or just trying to make your analytics queries run faster, understanding Parquet is a solid step. It’s a format that’s here to stay and keeps getting better.
Parquet files are like super-organized filing cabinets for lots of data. Instead of storing a whole row together, they store all the data for one column in one place, and all the data for another column in another place. This makes it much faster to find specific information, like just the 'price' of all items, without having to dig through every single detail of every item.
Parquet uses clever tricks to make files smaller. One trick is called 'dictionary encoding,' which is great when you have the same word or number repeated many times. Instead of writing the word over and over, it just writes a small number that points to the word in a special list. It also uses compression, like the kind you might use to zip up files on your computer, but it's specifically good at squishing similar data together.
Think of a Parquet file like a big book. A 'row group' is like a chapter, holding a bunch of related rows. Inside each chapter, the data for each column is stored separately in 'column chunks.' So, a column chunk is a piece of data for just one column, but only for the rows in that specific chapter (row group).
Yes, Parquet is really good at handling complex data. It can store things like lists of phone numbers for a person or even more detailed structures where one piece of information contains other pieces of information. It uses special 'levels' to keep track of how data is nested or repeated, making it easy to put back together later.
Reading Parquet files is designed to be efficient. When you ask for data, the system looks at the file's 'map' (metadata) to figure out exactly which pieces of data it needs. It can skip entire sections (row groups) or even parts of columns if they aren't relevant to your question. Then, it only reads and unpacks the necessary bits, making it much faster than reading a whole file.
Parquet became popular because it's super fast for asking questions about big datasets, especially for analysis. It also saves a lot of storage space. Because it's so good at these two things, many popular big data tools and platforms, like Spark and data lakes, decided to use it as their main way of storing data.
