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 file format, right? It's everywhere in data analytics these days, and for good reason. It's super efficient for storing tons of data and makes querying way faster. But what exactly makes it tick? We're going to break down how a parquet file actually works, from how it stores data to why it's so good at compressing things. It’s not as complicated as it sounds, and understanding it can really help you work with data more effectively.
So, what exactly is Apache Parquet? Think of it as a super-efficient way to store data, especially when you're dealing with big datasets. It's an open-source file format that organizes data in columns, not rows. This might sound like a small detail, but it makes a huge difference for how quickly you can access and process your information. It's designed to be a common format that different data tools can use, making it easier to share data between systems. It's pretty popular in the big data world, used with tools like Apache Spark and in cloud data warehouses.
Parquet has a few standout features that make it so useful:
Why should you bother with Parquet? Well, the advantages are pretty compelling, especially for analytics:
Parquet's design is all about making data storage and retrieval as efficient as possible for analytical workloads. It's not really meant for frequent updates to individual records, and it's not the most human-readable format out there, but for large-scale data analysis, it's hard to beat. The way it groups data into row groups and then stores columns together within those groups is key to its performance. This structure allows systems to skip over large amounts of data they don't need, which is a game-changer for big data.
While Parquet is fantastic for big data analytics, it's worth noting it might not be the best fit for very small datasets or scenarios requiring frequent real-time data modifications. Writing data can also take a bit longer due to the overhead of organizing it columnarly and applying compression. However, for its intended purpose, it's a solid choice.
So, how does Parquet actually organize all that data to make it so speedy? It's not just a big jumble of numbers and text. Parquet uses a clever, layered approach. Think of it like organizing a library, but for data.
First off, Parquet breaks your data down into what it calls "row groups." Each row group is basically a chunk of rows. This is a bit different from how you might think of data normally, where you look at one whole row at a time. Instead, Parquet takes a slice of your table, say, the first 10,000 rows, and makes that a row group. Then it does the same for the next 10,000, and so on. This is a horizontal partitioning of sorts.
Inside each of these row groups, the magic of columnar storage really kicks in. Instead of storing all the data for one row together, Parquet stores all the values for a single column together. So, all the 'user IDs' from that row group are stored next to each other, then all the 'product names', and so forth. This collection of data for one column within a row group is called a "column chunk." This is where the real efficiency gains start to show up.
Now, each of those column chunks isn't just one giant block of data. It's further divided into smaller units called "pages." These pages are the actual pieces of data that get compressed and encoded. Parquet can store statistics about the data within each page, like the minimum and maximum values. This is super handy because if a query only needs data within a certain range, Parquet can just skip over entire pages that don't contain any relevant information. It's like knowing exactly which shelves in the library to check without wandering down every aisle. This ability to skip data is a big part of why Parquet is so fast for analytics queries, allowing engines to fetch specific column values without reading the entire file.
Let's really hammer home why this columnar approach is so important. Imagine you have a table with millions of rows and dozens of columns. If you only need to know the average 'price' of items sold last month, a row-based format would have to read through every single row, picking out the 'price' value and ignoring all the other data in that row. That's a lot of wasted effort.
With Parquet's columnar structure, it only needs to read the 'price' column chunk(s) for the relevant row groups. All the other columns? They're just ignored. This drastically cuts down on the amount of data that needs to be read from disk or sent over the network. It's a game-changer for performance, especially with massive datasets. This is why Parquet is so popular for big data analytics.
The way Parquet structures data into row groups, then column chunks, and finally pages, is the foundation of its efficiency. It allows for targeted data access and effective compression, making it a top choice for storing large analytical datasets.
Here's a quick look at how the structure breaks down:
This layered organization is key to Parquet's ability to handle large volumes of data efficiently and speed up queries. It's a smart design that pays off big time when you're working with serious amounts of information.
When you're working with data, it's easy to think of things like "string" or "date" as basic building blocks. Parquet makes a distinction between how we think about data (logical types) and how it's actually stored on disk (physical types). This separation is pretty neat because it lets Parquet use a small set of physical types to represent a much wider range of data concepts.
Think of physical types as the raw ingredients Parquet knows how to handle directly. These are things like INT32 (a 32-bit whole number), DOUBLE (a 64-bit floating-point number), or BYTE_ARRAY (a chunk of raw bytes). These are the fundamental building blocks.
Logical types, on the other hand, add meaning to these physical types. For example, a STRING isn't a distinct physical type; it's usually stored as a BYTE_ARRAY that the system knows to interpret as UTF-8 encoded text. Similarly, a DATE might be stored as an INT32 representing the number of days since a specific starting point (like the Unix epoch). This allows for richer data representation without complicating the core storage mechanism.
The way data is encoded and compressed in Parquet heavily relies on its physical type. Different physical types have different characteristics that make them suitable for various encoding schemes. For instance, a column of integers might benefit greatly from run-length encoding (RLE) if there are many repeating values, while a column of random strings might be better off with dictionary encoding or even plain encoding.
Here's a quick look at some common physical types and how they might be handled:
The choice of encoding is a trade-off. While dictionary encoding can shrink data significantly, it adds a bit of overhead to read. Plain encoding is simple but might not save much space. Parquet's engine tries to pick the best encoding based on the data's characteristics.
Parquet doesn't just stop at simple numbers and text. It's built to handle more intricate data structures, which is a big deal for modern data analysis. This means you can store things like lists (arrays), key-value pairs (maps), and nested records (structs) directly within your Parquet files.
This ability to natively support complex types means you don't have to pre-process your data into a flat format before storing it, which saves time and preserves the original structure of your information. It makes working with semi-structured or complex data much more straightforward.
So, we've talked about how Parquet stores data in columns, which is already a big win for performance. But the real magic happens with how it packs that data down. Parquet doesn't just store your data; it tries to make it as small as possible using clever tricks for compression and encoding. This is where you really see the savings in storage costs and the speed-up in queries.
Parquet uses a few different ways to represent data within a column chunk before it even gets compressed. Think of these as different packing methods. The best one to use depends a lot on the data itself. For example, if you have a column with lots of repeated values, like 'USA', 'Canada', 'USA', 'Mexico', 'USA', Parquet can use something called Dictionary Encoding. It creates a small dictionary of unique values and then just stores numbers that point to those dictionary entries. So instead of writing 'USA' three times, it might write '0', '1', '0', '2', '0' (if 'USA' is 0, 'Canada' is 1, etc.). This can shrink the data a ton.
Other schemes include:
Once the data is encoded, Parquet applies a compression codec. This is like putting the packed box into a vacuum-sealed bag. You have choices here, and each has its pros and cons:
| Codec | Compression Speed | Decompression Speed | Compression Ratio | CPU Usage | Typical Use Case |
| :-------- | :---------------- | :------------------ | :---------------- | :-------- | :------------------------------------------------ | --- |
| Snappy | Very Fast | Very Fast | Good | Low | Real-time ingestion, when CPU is a bottleneck |
| Gzip | Moderate | Moderate | Better | Moderate | General purpose, good balance |
| Zstandard | Fast | Fast | Very Good | Moderate | Cloud storage cost savings, analytical workloads |
| LZO | Fast | Fast | Good | Low | Similar to Snappy, often used in Hadoop |
Choosing the right codec is a balancing act. Snappy is super quick, which is great if your system is bogged down by CPU work. But if you're paying a lot for cloud storage and want to save money, Zstandard often gives you a smaller file size for a bit more CPU effort during compression and decompression. Gzip is a solid middle-ground option.
The decision between compression codecs often boils down to whether your bottleneck is CPU or storage cost. For many cloud-based data lakes, prioritizing storage savings with codecs like Zstandard can lead to significant long-term cost reductions, even if it means slightly longer processing times.
Why is compressing columns so much better than compressing rows? Well, remember how data in the same column tends to be similar? When you compress a column chunk, you're feeding the compressor a lot of similar data. This makes the compression algorithms work much more effectively. Think about trying to compress a book page by page versus compressing all the chapters about 'dragons' together. Compressing the 'dragon' chapters would likely yield much better results because all that related content is grouped. Parquet does this naturally because it stores all the 'dragon' data (or all the 'customer IDs', or all the 'timestamps') together. This leads to significantly smaller file sizes compared to row-based formats like CSV, where you'd have to compress a mix of different data types and values for each row.
So, Parquet isn't just about storing data; it's about storing it smartly. When you're dealing with massive datasets, how you access that data makes a huge difference. Parquet has a few tricks up its sleeve to speed things up, especially when you're running queries.
Imagine you have a huge table with, say, 100 columns, but your query only needs data from two of them. With older, row-based formats, the system might have to read through all 100 columns for every single row, even if it only needs a tiny bit of info. That's a lot of wasted effort and time. Parquet, being columnar, lets the query engine skip over all the columns it doesn't need. This is called column pruning, and it dramatically cuts down on the amount of data that needs to be read from disk or cloud storage. Less reading means faster queries and lower costs, especially in cloud environments where you pay for data scanned.
This is another big one. Predicate pushdown is like filtering data as early as possible. If your query has a WHERE clause, like WHERE year = 2024, Parquet can use the metadata it stores (like min/max values for each column chunk) to figure out which parts of the file definitely don't contain data matching your filter. It can then skip reading those entire sections. Think of it as having a really good index for your data, but built right into the file format itself. This means the query engine gets a much smaller set of data to actually process, leading to significant speedups.
Parquet files are broken down into 'row groups'. These are like internal divisions within the file. The size of these row groups matters. If they're too small, you end up with a lot of overhead and many small files, which can slow down queries because the system has to manage all those little pieces. If they're too big, you lose some of the benefits of columnar processing and might not get the best performance for certain types of queries, especially if you're only reading a small portion of the data. The sweet spot is generally considered to be between 128 MB and 1 GB. Getting this right helps balance the efficiency of reading columns with the overhead of managing the file structure.
The way data is organized within a Parquet file directly impacts how quickly you can get answers from it. By intelligently skipping unnecessary data and filtering early, Parquet helps make big data analytics much more efficient and cost-effective.
So, you've got this big data pipeline chugging along, storing tons of information in Parquet files. Then, someone decides we need a new data point, or maybe an existing one needs to change. What happens? With Parquet, you don't usually have to freak out and rewrite everything. It's designed to handle these kinds of changes, which is a pretty big deal when you're dealing with massive datasets. You can add new columns, and older versions of your data just won't have them, which is fine. Or, you can change the order of columns. The format is smart enough to keep things working without a massive data migration project. This flexibility means your data pipelines can keep running without constant, costly interruptions just because the data structure shifted a bit.
Parquet isn't just some niche format that only one tool understands. It's pretty much everywhere in the big data world. Whether you're using Python, Java, or even Rust, there are libraries to read and write Parquet. Big platforms like Spark, Flink, and cloud services all play nicely with it. Plus, it's the foundation for other popular table formats like Delta Lake and Apache Iceberg. This widespread adoption means you can move your data around and use different tools without a lot of hassle. It's like a universal adapter for data storage.
When people talk about "lakehouses" – that blend of data lakes and data warehouses – Parquet is often the main storage format underneath. Think of it as the building blocks. Formats like Delta Lake and Iceberg build on top of Parquet files, adding features like transaction logs for reliability (ACID properties), time travel (going back to previous versions of your data), and better ways to manage partitions. So, while Parquet itself is the efficient storage layer, these other formats use it to provide more advanced data management capabilities, making the whole lakehouse concept work smoothly.
The ability to adapt to changing data requirements without massive data rewrites is a significant advantage. It allows systems to remain agile and responsive to new analytical needs or data sources.
When you're dealing with big data, security isn't just an afterthought; it's a necessity. Parquet files, while great for performance, aren't inherently secure out of the box. You have to actively set things up to protect your data. Think of it like leaving your house unlocked versus putting a good deadbolt on the door – the house is still there, but one is much safer.
Parquet has this thing called a modular encryption framework. It's pretty neat because it lets you encrypt specific parts of your data, not just the whole file. This is super useful if, say, you have customer names in one column and transaction amounts in another. You might want to encrypt the names more heavily or with different keys than the transaction amounts, especially if different teams need access to different pieces of information. It uses a system where keys encrypt other keys, which eventually leads to a master key kept somewhere safe, like a key management service. This way, the actual keys used for encryption aren't just sitting there in the file itself.
This is where the modular framework really shines. You can actually encrypt individual columns with their own keys. This means you can have sensitive columns like personally identifiable information (PII) locked down tight, while less sensitive columns, like timestamps or product IDs, might have less stringent encryption or even be accessible to more people. It's all about granular control. For example, financial data might get one set of keys, while user IDs get another. This approach helps meet compliance rules, like data sovereignty, where certain data types need to be handled differently depending on where they're stored or who can access them.
So, where do you keep all these keys? Parquet integrates with Key Management Services (KMS). This is a big deal. Instead of managing keys yourself, which is a headache and prone to errors, you can use a dedicated service. This service handles the creation, storage, and rotation of your encryption keys. It's generally more secure and makes your life a lot easier. Trying to manage encryption keys manually across a large data system is a recipe for disaster, trust me. Integrating with a KMS means you can often reduce the overhead of key management significantly, sometimes by a good chunk, while still keeping things compliant and secure.
It's important to remember that even with encryption, you still need other security measures. Think of it like having a strong lock on your door but also making sure you don't leave your keys lying around or tell strangers your alarm code. Input validation and keeping your software patched are still super important. A malicious file could still cause problems if not checked properly before it even gets encrypted.
Here's a quick look at how different encryption modes might affect things:
And when it comes to managing those keys:
So, we've gone through what makes Parquet tick. It's not just another file format; it's a smart way to store data that really helps when you're dealing with big amounts of it. By organizing data by column and using clever compression, Parquet saves space and makes getting your data back super fast. It's become a go-to for a lot of data tools and platforms for good reason. While it might not be the best fit for every single tiny task, for most big data jobs, it's a solid choice that keeps things running smoothly and saves you money on storage. It's definitely worth understanding how it works to make your data projects work better.
Think of Parquet as a super-organized way to store big piles of data, especially for computers doing lots of analysis. Instead of writing down every single detail about one person all together, it groups all the ages together, all the names together, and so on. This makes it much faster to find and work with specific pieces of information when you need them.
Imagine you have a big spreadsheet and you only need to know everyone's age. If the data is stored row by row, the computer has to read through all the other information (like names, addresses, etc.) for each person just to get to the age. But if it's stored column by column, it can just zoom right to the 'age' column and grab all the ages without looking at anything else. This saves a ton of time and effort.
Yes, definitely! Because all the data in a column is usually the same type (like all numbers or all words), it's easier to squish it down using special tricks called compression. It's like packing clothes tightly in a suitcase. This means Parquet files take up much less room, which saves money on storage, especially in the cloud.
Parquet is pretty good at handling changes. If you need to add a new type of information later, like a new category or a new measurement, you can usually do it without having to reorganize all your old data. This is called 'schema evolution' and it's a big help when your data needs grow over time.
Parquet has ways to make your data more secure. It can even lock up specific columns of data, so only people with the right key can see certain sensitive details. However, you need to set up these security features yourself and manage your keys carefully to keep your data truly safe.
Parquet shines when you're working with massive amounts of data and need to do lots of analysis quickly. But if you have a very small amount of data, or if you need to change individual records very often (like updating a single customer's address constantly), other file types might be simpler or more suitable. It's also not the best for super-fast, real-time updates.
