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 big data and how it's changing things. A big part of making all that data work is using the right file formats. Today, we're going to talk about one that's pretty popular: Parquet files. They're not exactly new, but they're a solid choice for handling large amounts of information, especially when you need to get insights out of it quickly. Think of them as a really organized way to store data that makes it easier for computers to read and process.
So, what exactly is this Parquet thing we keep hearing about in the big data world? Simply put, Apache Parquet is a way to organize and store data, especially when you're dealing with a lot of it. Think of it as a super-efficient filing system for your digital information. It's not just another file format; it's designed from the ground up to make working with massive datasets faster and less of a headache. It's become a go-to standard for analytics because it handles data in a way that speeds up queries and saves storage space.
This is where Parquet really shines. Unlike older formats that store data row by row (like a spreadsheet where you see all the info for one person on a single line), Parquet stores data column by column. Imagine you have a table with columns like 'Name', 'Age', and 'City'. Instead of storing:
Parquet would store all the 'Name' data together, then all the 'Age' data together, and then all the 'City' data together.
Why does this matter? Well, when you're running an analysis, you often only need a few columns, right? If you're looking for everyone over 30, you only need the 'Age' column. With Parquet, the system can just grab all the 'Age' data without having to sift through all the 'Name' and 'City' information. This means way less data to read, which translates to faster queries and less work for your computer.
One of the big reasons Parquet is so popular is that it's open-source. This means anyone can use it, modify it, and build tools around it without paying hefty license fees. It's supported by a large community, which means it's constantly being improved and integrated with other popular big data tools like Spark and Hadoop. Plus, Parquet files are 'self-describing'. This means that each file contains not just the data itself, but also information about the data's structure (like column names and types) and how it's organized. This makes it easier for different systems to read and understand the data without needing separate documentation.
The self-describing nature means that when you open a Parquet file, it tells you what's inside and how it's laid out. This metadata is built right into the file, making it simpler to manage and share data across different applications and teams.
Parquet files have a specific way of organizing data that makes them super efficient for big data tasks. It's not just a jumbled mess of rows and columns; there's a clear hierarchy at play. Think of it like a well-organized filing cabinet.
First off, Parquet breaks down your data into what it calls "row groups." These are basically large chunks of data. Instead of storing all the data for one row together, a row group holds data for a subset of rows, but it keeps all the data for a single column together within that group. This is a big deal for performance. Each row group also keeps track of things like the minimum and maximum values for each column within it. This means if you're looking for, say, records where the 'age' is over 50, and a row group's 'age' column only has values between 20 and 40, the system can just skip that entire group without even looking at the data inside. Pretty neat, right?
Within each row group, the data for each individual column is stored separately. These are called "column chunks." So, if you have a table with columns like 'user_id', 'timestamp', and 'event_type', a row group will have a column chunk for 'user_id', another for 'timestamp', and a third for 'event_type'. This is where the columnar storage really shines. Because all the data for a single column is together, it's much easier to read just what you need for a specific query.
Now, each column chunk is further divided into smaller pieces called "pages." These are the smallest units of storage in a Parquet file. There are a couple of types of pages:
So, to recap, it's a layered structure: a Parquet file contains multiple row groups, each row group contains column chunks for each column, and each column chunk is made up of pages. This organization is key to why Parquet is so fast for analytics.
The way Parquet structures data, grouping columns together and then breaking them into smaller pages, is what allows query engines to be so selective. They can pinpoint exactly which bits of data need to be read, ignoring everything else. This drastically cuts down on the amount of work the system has to do.
When you're dealing with big data, how fast you can get to your information really matters. Parquet files shine here because of how they store data. Instead of keeping all the info for one record together, like in a CSV, Parquet keeps all the data for a single column together. This might sound like a small difference, but it makes a huge impact on speed.
Think about running a report that only needs a few columns from a massive table. With Parquet, the system can just grab those specific columns and ignore the rest. This means way less data has to be read from storage. It's like only pulling the books you need from a library shelf instead of emptying the whole shelf. This makes analytical queries run much faster, especially when you're working with datasets that have tons of columns. It's a big reason why Parquet is so popular for big data analytics.
Because Parquet reads data column by column, it drastically cuts down on Input/Output (I/O) operations. Less I/O means your system isn't bogged down waiting for data to load. This is super important for performance. Imagine you have a table with a hundred columns, but your query only needs three. A row-based format would still have to read through all hundred columns for every single row. Parquet, however, only reads the three columns you asked for. This efficiency is a game-changer for large datasets.
Parquet files are built with analytical workloads in mind. These are the kinds of tasks where you're constantly querying and analyzing data, but not changing it very often. The columnar structure means that when you run queries, you're only accessing the data you need. This makes reading data incredibly fast. It's not the best choice if you're constantly writing new rows of data, but for looking at existing data, it's hard to beat.
Here's a quick look at how the structure helps:
The way Parquet organizes data means that systems can skip reading large amounts of data that aren't relevant to a specific query. This selective reading is a major factor in its speed.
This design makes Parquet a go-to format for data lakes and analytical databases where fast querying is a top priority.
Parquet files really shine when it comes to making your data smaller and faster to access, and a big part of that is how they handle compression and encoding. Unlike older formats that might just slap a single compression method on the whole file, Parquet is way more flexible. It can apply different techniques to different columns, which makes a lot of sense when you think about it – numbers and text don't always compress the same way, right?
Parquet lets you pick from a few different compression algorithms. You've got options like Snappy, GZIP, and LZO, and more recently, ZSTD has become a popular choice. ZSTD is pretty neat because it often gives you better compression ratios than GZIP but with much faster decompression speeds, which is a win-win for many analytical tasks. The choice often comes down to balancing how much you want to shrink the file size versus how quickly you need to read the data back.
This is one of those clever tricks that Parquet uses. If a column has a lot of the same values – think of a 'country' column where most entries might be 'USA' or 'Canada' – dictionary encoding comes into play. Instead of writing 'USA' over and over, Parquet creates a small dictionary of unique values for that column and then just stores references (like numbers) to those values. This can drastically cut down on storage space when you have high cardinality in certain columns.
Parquet also uses techniques like bit packing and RLE. Bit packing is useful for storing integers. If you have a bunch of small numbers, say between 0 and 100, you don't need a full 32 or 64 bits to store each one. Bit packing lets Parquet use just enough bits for the largest number in that set, saving space. Run-length encoding (RLE) is great for sequences where the same value repeats many times in a row. Instead of listing 'AAAAA', RLE might store it as 'A' followed by the number 5. Parquet often combines these methods, intelligently switching between them to get the best compression for specific data patterns.
The internal workings of Parquet's compression and encoding are designed to be smart. They analyze the data within columns and apply the most efficient method automatically. This means you often get great results without having to manually configure every little detail, which is a huge time-saver when dealing with massive datasets.
Here's a quick look at how these might apply:
By using these techniques, Parquet files can become significantly smaller, which not only saves on storage costs but also speeds up data transfer and processing times because less data needs to be read from disk.
When you're building out systems to handle big data, the file format you choose really matters. It's not just about stuffing data somewhere; it's about making sure you can actually get to it and use it efficiently later. Parquet has become a go-to choice for a lot of these modern setups, and for good reason.
Think of data lakes and lakehouses as massive storage areas for all sorts of data. Parquet fits right in here. Because it's open-source and not tied to one specific vendor, it plays nicely with different tools. This means you can store your data in a Parquet file and then use various query engines to analyze it, rather than being stuck with whatever a particular database system uses. It's a big deal for flexibility.
Storing data in open formats like Parquet gives you a lot more freedom. You're not tied to a single company's technology, which can save a lot of headaches and costs down the road.
Parquet wasn't just created in a vacuum; it's part of the Apache ecosystem. This means it works really well with popular big data processing frameworks like Spark, Hadoop, and Flink. These frameworks are designed to crunch massive amounts of data, and Parquet's structure makes that job much easier. For example, Spark can read Parquet files very quickly because it knows how the data is organized column by column. This makes processing and transforming data much faster.
This is a big one. Many traditional data warehouses use proprietary formats. If you store your data in one of those, you're pretty much stuck with that vendor. Parquet, being an open-source project, breaks you free from that. You can use Parquet files in your data lake or lakehouse and then choose the best query engine or processing tool for the job, whether that's Presto, Hive, or something else entirely. This adaptability is key for long-term data strategy. You can find more about why columnar storage is efficient for analytics.
Working with data means things change, right? Schemas aren't always set in stone forever. You might start with a basic set of columns and then, down the line, realize you need to add more information. Parquet files are pretty good at handling this.
Parquet is designed to let you add new columns to your data over time. This is super handy because you don't have to rewrite all your old data files just to add a new field. You can have a bunch of Parquet files sitting around, each with a slightly different but compatible schema, and Parquet can usually figure out how to merge them when you query. It's not magic, though; you still need to be a bit thoughtful about how you change things. For instance, renaming columns isn't directly supported, and adding a column that absolutely must have a value (a non-nullable column) can be tricky.
The ability to adapt schemas without breaking existing data pipelines is a big win for keeping projects moving forward without constant data wrangling headaches.
Parquet doesn't just do simple numbers and text. It can handle nested structures like arrays, maps, and structs. This means you can represent more complicated data relationships directly within your files, which is great for things like JSON data or deeply nested logs. This makes it easier to work with rich datasets without having to flatten everything out first. You can find more about how formats like this support schema evolution in modern data pipelines here.
Once a Parquet file is written, it's generally considered immutable. You can't just go in and change a single value in an existing file. If you need to update data, you typically write a new version of the file with the corrected information. This immutability, combined with schema evolution, helps maintain data integrity and makes it easier to track changes over time, especially in large-scale data lakes and lakehouses. It's a core part of how Parquet fits into robust data architectures.
So, you've been hearing a lot about Parquet files, and you're wondering if they're the right fit for your data projects. It's a good question to ask, because not every file format is suited for every job. Think of it like choosing the right tool for a specific task – you wouldn't use a hammer to screw in a bolt, right? Parquet shines in certain situations, and knowing when to use it can save you a lot of headaches and speed up your work.
If you're dealing with a mountain of data, like millions or even billions of rows, Parquet is definitely worth a serious look. Its design is all about handling big chunks of information efficiently. When you've got that much data, the way it's stored makes a huge difference in how fast you can get answers. Parquet is built for performance and effective compression, which is a big deal when you're working with massive datasets. It's highly recommended for large-scale analytics and big data queries involving millions or billions of rows. Its design prioritizes query performance and I/O efficiency, making it a suitable choice when dealing with massive datasets. Parquet is highly recommended
This is where Parquet really shows its strengths. If your main goal is to run analytical queries – you know, the kind where you're slicing and dicing data, looking for trends, or aggregating information – Parquet is a fantastic choice. Unlike older formats that make you read through entire rows even if you only need a few pieces of information, Parquet's columnar approach means you only pull the data you actually need. This dramatically cuts down on the amount of data that needs to be processed, making your queries run much faster. It's especially useful when your datasets have a lot of columns, but your queries typically only involve a small subset of them.
Beyond just speed, Parquet is also pretty good at saving space. It uses clever compression and encoding techniques, like dictionary encoding for repeated values and run-length encoding, to shrink your data down. This means you can store more data in less space, which can be a significant cost saver, especially if you're paying for storage. Plus, when you need to move data around or load it into memory for analysis, smaller files mean less I/O, which again, speeds things up.
Here's a quick rundown of when Parquet really makes sense:
Choosing Parquet means you're opting for a format that's designed from the ground up for the demands of modern big data analytics. It's not just about storing data; it's about making that data accessible and usable in the most efficient way possible, especially when dealing with large, complex datasets that need to be queried frequently.
So, we've gone through what makes Parquet files tick. It's pretty clear why they've become so popular for handling big data. The way they store data column by column really helps speed things up when you're trying to analyze stuff, and it means you don't have to load as much data from your storage. Plus, the built-in ways to shrink file sizes are a big help too. While it might seem a bit much to get started with, especially if you're new to this, the benefits for serious data work are pretty significant. If you're dealing with lots of data and need to get insights from it quickly, Parquet is definitely worth looking into. It's a solid choice for keeping your data organized and accessible.
Think of a Parquet file as a super-organized way to store lots of information, especially for computers doing big calculations. Instead of storing data like a list where each item is a whole record (like a row in a spreadsheet), Parquet stores data column by column. This makes it way faster to find and use specific pieces of information when you need them for analysis.
Imagine you have a huge spreadsheet with hundreds of columns, but you only need to look at the 'Sales' column for your report. If the data was stored like a regular list (row by row), the computer would have to sift through every single piece of data for every row, even the stuff it doesn't need. With Parquet's column storage, it can just grab the 'Sales' column data directly, saving a ton of time and effort.
Yes, they can! Parquet is smart about handling changes. If you start with a file that has certain columns and later add more columns to your data, Parquet can usually figure out how to combine these files even if their structures aren't exactly the same. This is super helpful because data often grows and changes.
Parquet uses clever tricks to shrink file sizes. It can use different methods depending on the type of data. For example, if a column has lots of the same value repeated, it can just store that value once and note how many times it appears. It also has other techniques like bit packing and run-length encoding that help make files more compact, which saves storage space.
Not at all! Parquet is designed to be open and play well with others. Many popular big data tools and platforms, like Spark and data lakes, use Parquet. Because it's not tied to one specific company's product, you have more freedom to choose the tools you want to use.
Parquet is a great choice when you're dealing with massive amounts of data and need to run analytical queries – meaning you're asking questions of your data to find patterns or insights. It's also ideal if you want to save on storage costs because its compression methods are very effective.
