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 the parquet format, right? It's this thing that data folks use to store information, and honestly, it's pretty good at it. Think of it like a super-organized filing cabinet for your data, way better than just stuffing papers into a big box. We're going to break down what makes this parquet format so special, why it's used so much, and how it stacks up against other ways of saving data. It's all about making sure your data is easy to find and quick to use, especially when you have a ton of it.
So, what's really going on under the hood with Parquet? It's not just some magic box that makes your data smaller and faster. It's built on a few smart ideas about how to organize information.
Forget how you might write things down in a spreadsheet, row by row. Parquet flips that. Instead of storing data like Row 1: A, B, C, Row 2: D, E, F, it stores it like Column A: A, D, Column B: B, E, Column C: C, F. This might seem like a small change, but it's a big deal for how quickly you can get to specific pieces of information. When you're running a query that only needs, say, column 'B', Parquet can just grab that 'B' data without even looking at 'A' or 'C'. This makes reading specific columns way faster, especially when you have tons of data.
Parquet uses something called a "hybrid" layout. Think of it like this: it's not purely row-based, and it's not purely column-based. It breaks your data into chunks, called "row groups." Within each row group, it then stores the data column by column. So, you have a block of data for column 1, then a block for column 2, and so on, for a certain number of rows. Then it repeats for the next set of rows. This approach tries to get the best of both worlds, making it good for analytical tasks where you often look at specific columns across many rows.
Here's a simplified look at how it's structured:
.parquet files. This helps with managing large datasets.The way Parquet organizes data into row groups and then columns within those groups is key. It means that similar data types are stored close together, which is a huge win for compression and for quickly finding what you need. If you're only interested in a few columns, Parquet can often skip entire row groups or even files based on some clever metadata it keeps.
To wrap it up, Parquet has a clear hierarchy. At the top level, you have your data spread across potentially many .parquet files. Each file is then broken down into row groups. Within each row group, the data is organized by columns. This layered structure allows Parquet to be very flexible and efficient. It means that when you ask for data, Parquet doesn't have to read the whole file. It can use the metadata it stores to figure out exactly which files, row groups, and columns contain the information you're looking for, and then just read that small piece. It's like having a super-organized library where you can find any book without searching every shelf.
Parquet isn't just about storing data; it's about storing it smartly. Several built-in features work together to make querying and managing large datasets way faster and cheaper. Think of it like organizing your garage – you don't just throw everything in a pile, right? You group similar items, use labels, and put things you use often within easy reach. Parquet does something similar for your data.
One of the coolest things Parquet does is use different ways to represent your data, depending on what the data actually looks like. It's not a one-size-fits-all approach. This means Parquet can shrink the amount of space your data takes up significantly.
These encoding methods are applied at the column level, meaning Parquet analyzes each column individually to pick the best encoding strategy. This granular approach is a big reason why Parquet files are often much smaller than other formats.
Beyond encoding, Parquet also lets you apply compression to the data pages. This is like zipping up files on your computer, but it happens automatically as the data is written. Parquet supports several compression codecs, and you can often choose which one to use.
Here's a look at some common ones:
Choosing the right compression codec often depends on your priorities: speed of reads/writes versus how much you want to save on storage space. For many, Zstandard is becoming a popular choice due to its strong performance.
This is where Parquet really shines for analytics. Predicate pushdown means that filters you apply in your query (like WHERE age > 30) are pushed down to the storage layer. Instead of reading the entire dataset and then filtering, Parquet can often skip reading entire blocks of data that don't match your filter criteria.
How does it work?
WHERE price < 10 and the min/max for a chunk is 100 to 500, Parquet knows immediately that no data in that chunk can possibly match your filter, and it skips reading it entirely.This combination of column pruning and predicate pushdown dramatically reduces the amount of data that needs to be read from disk or network, leading to much faster query times, especially on very large datasets.
So, how does Parquet actually store all this data? It's not just about putting numbers and text into a file; there's a bit more thought behind it to keep things speedy and small. Parquet makes a clear distinction between what the data means and how it's physically stored.
Think of it like this: a 'date' is what we understand it to be (logical type), but on disk, it might be stored as a number representing days since a certain point (physical type). Parquet does this for many data types. It keeps the physical types limited to make reading and writing simpler, but it maps these to more descriptive logical types. This way, you can have a 'date' or 'timestamp' without the file format needing to know every single variation of how dates can be represented.
Here's a quick look at how some common logical types map to physical ones:
Parquet uses clever tricks to shrink data. Two big ones are Dictionary Encoding and Run-Length Encoding (RLE).
When Parquet uses dictionary encoding, it often stores those small integer codes. To make them even smaller, it uses something called bit-packing. Basically, it figures out the minimum number of bits needed to represent all the numbers in your dictionary and uses exactly that many bits for each value. This is super efficient for storing those dictionary codes, especially when you have many unique values but they still fit within a limited range.
Parquet's design prioritizes efficiency by choosing storage methods that exploit common data patterns. By separating logical meaning from physical storage and employing smart encoding strategies, it significantly reduces file sizes and speeds up data retrieval, making it a top choice for analytical workloads.
These techniques work together. For instance, dictionary encoding might be used first, and then the resulting codes are bit-packed for maximum space savings. It's all about finding the most compact way to represent the data on disk without making it a pain to read back.
Parquet isn't just about how data is laid out on disk; it's also smart about how it keeps track of what's inside those files. This is where metadata comes in, acting like a helpful index that lets systems quickly figure out what data they actually need to read. This ability to skip over irrelevant data is a huge part of why Parquet is so fast for analytics.
Think of a Parquet file as a big book. Instead of reading every single page to find what you're looking for, metadata provides a table of contents and an index. For each chunk of data (called a row group), Parquet stores information about the values within that chunk. This includes things like the minimum and maximum values for each column in that row group. When you run a query with a filter, like WHERE age > 30, the system can look at this metadata. If a whole row group has ages ranging from 10 to 25, the system knows it doesn't need to even open that chunk of data. It just skips it. This is a massive time saver, especially with huge datasets. It's a core reason why Parquet is so popular for data lakes and lakehouses.
This brings us to predicate pushdown. It's a fancy term for pushing the filtering logic down to the data storage level. Parquet's metadata, specifically the min/max statistics stored for each column within a row group, makes this possible. When a query comes in with a WHERE clause (the predicate), the query engine checks the metadata. If the predicate can't possibly be met by the data in a specific row group (e.g., the query asks for values less than 10, but the min value in the row group is 20), that entire row group is skipped. This process significantly reduces the amount of data that needs to be read from disk or network, speeding up queries dramatically. It's a key feature that helps Parquet perform so well compared to older formats. You can find more details on how these optimizations work in various big data processing frameworks.
Parquet also stores schema information within the file itself. This means that when you open a Parquet file, the system automatically knows the structure of the data – the column names, their data types, and how they are organized. This is called schema inference, and it makes working with data much easier because you don't have to manually define the schema every time. Furthermore, Parquet is designed to handle changes to the schema over time, a concept known as schema evolution. You can add new columns or change data types without breaking older data or requiring complex data migrations. This flexibility is super important for systems that are constantly changing, like streaming data pipelines or machine learning feature stores. It means your data storage can adapt as your needs change, without a lot of hassle.
So, you've heard about Parquet and how great it is, but how does it stack up against other ways of storing data? It's not just about picking a format; it's about picking the right one for what you're trying to do. Think of it like choosing tools for a job – you wouldn't use a hammer to screw in a bolt, right?
Row-based formats, like the ever-present CSV or Avro, store data one row at a time. Imagine a spreadsheet – each row is a complete record. This is fine for some things, like simple data exchange or when you need to read an entire record at once. But when you're doing analytics, especially on big datasets, this approach can be a real drag.
Why? Because if your analysis only needs a few columns, you still have to read through all the other columns in each row. It's like trying to find a specific ingredient in a grocery store by walking down every single aisle, even if you only need something from the produce section. Parquet, on the other hand, stores data by column. So, if you only need, say, customer ages, it can just grab all the age data without touching any other information. This columnar approach is a game-changer for query speed and storage efficiency in analytical workloads.
Here's a quick look:
Okay, so we know columnar is good. But is Parquet the only columnar format out there? Nope. Another big player is ORC (Optimized Row Columnar). Both are designed for similar analytical tasks and share many benefits, like columnar storage, good compression, and predicate pushdown. They both do a solid job of making analytical queries faster and storage smaller compared to row formats.
However, there are some differences in how they implement things, which can sometimes lead to slight performance variations depending on the specific workload or the tools you're using. Parquet often gets a nod for its broad support across different big data frameworks and its flexible encoding options. ORC, on the other hand, has historically been very popular in the Hive ecosystem.
Choosing between Parquet and ORC often comes down to the specific tools and platforms you're working with. Both are excellent columnar formats, but their underlying implementations and ecosystem support can sometimes tip the scales for certain use cases.
Now, things get a bit more interesting when we talk about table formats like Apache Iceberg and Delta Lake. These aren't file formats themselves, like Parquet or ORC. Instead, they sit on top of file formats (often Parquet) and add a whole layer of management and features. Think of Parquet as the bricks and mortar, and Iceberg or Delta Lake as the architect and construction manager.
These table formats bring features like:
So, you'll often see Parquet files being used within an Iceberg or Delta Lake table. They provide the efficient storage, and the table format provides the robust management layer. It's a powerful combination for building modern data lakes and lakehouses.
So, where does this Parquet format really shine? It's not just some theoretical thing; companies are actually using it for some pretty big jobs. Think about it – when you're dealing with massive amounts of data, how you store it makes a huge difference in how fast you can get answers.
This is probably where Parquet gets the most love. Companies like Airbnb and Uber, and even big retailers, use Parquet to store all their data in places like S3 or Azure. It's a smart way to keep data organized and accessible, but also fast enough for analysis. It's like having a giant, well-organized library for all your information. Modern setups often use Parquet's encryption features to keep things secure while still being able to pull out just the data you need quickly. This combination of scalability and security is what makes lakehouses work so well.
If you're using tools like Tableau, Power BI, or Apache Superset to look at your data, they often talk to Parquet files. When these tools query Parquet, they can be really efficient. They don't have to read through every single piece of data; they can just grab the columns they need. This means your dashboards load faster and you spend less money on cloud storage because less data is being scanned.
For folks working with machine learning, Parquet is a lifesaver. When you're training a model, you often only need specific pieces of information, or 'features'. Parquet lets you load just those columns you're interested in, super fast. This speeds up the whole training process. Plus, it's great for storing embeddings, which are a key part of many AI models.
Even when data is coming in constantly, like from Kafka or Kinesis, Parquet plays a role. You can take those streams and convert them into Parquet files. This makes it easier to process that data later in batches. It's a common pattern for handling real-time data and making it ready for analysis without losing anything.
Parquet's strength lies in its ability to handle large datasets efficiently. It's designed for analytical workloads where you're reading specific columns, not necessarily entire rows. This makes it less ideal for situations where you're constantly updating individual records, where other formats might be a better fit.
So, we've gone through what makes Parquet tick. It’s pretty clear why it’s become such a big deal in handling large amounts of data. By storing information in columns instead of rows, it just makes sense for a lot of the analytical tasks we do. Plus, the way it compresses data and uses clever encoding tricks means less storage space and faster queries. Whether you're building data lakes, dashboards, or machine learning pipelines, understanding Parquet helps make things run smoother and faster. It’s not just another file format; it’s a tool that really helps manage data efficiently.
Think of Parquet as a special way to save data, like organizing your toys. Instead of putting all the toys from one play session together (like in a row), Parquet puts all the cars together, all the dolls together, and so on (like in columns). This makes it super fast to find just the cars when you need them for a race, without digging through all the other toys.
When you ask for information, you usually only need certain types of data. If all your data is saved like a phone book (one entry per person, with all their info), you have to read the whole entry even if you only wanted their phone number. With columns, Parquet lets computers grab just the phone numbers without reading addresses or birthdays, saving a lot of time and effort.
Because Parquet groups similar data together (like all the numbers in one column), it can use smart tricks to make the files smaller. Imagine if you had a list of 1000 zeros. Instead of writing '0' a thousand times, Parquet can just say 'there are 1000 zeros here.' It also uses different ways to squish the data down, like using different types of packing for different kinds of items.
Imagine a big book. A 'row group' is like a chapter, holding a bunch of related pages (rows) of information. Inside that chapter, 'column chunks' are like sections dedicated to a single topic, such as all the names or all the ages from those pages. This helps organize the data so it's easier to find and read.
Yes! Parquet is pretty flexible. If you decide to add a new category of information later, like adding a 'favorite color' column, Parquet can handle that without you having to reorganize all your old data. It's like adding a new section to your organized toy bins without messing up the old ones.
Parquet is fantastic for big collections of data that you'll be analyzing a lot, like in data lakes or when making reports and dashboards. It's also great for machine learning projects where you need to quickly access specific pieces of information. If you're dealing with lots of data and speed matters, Parquet is usually a top choice.
