Unlock Success: Mastering Instagram Ads Best Practices for 2026
Master Instagram ads best practices for 2026. Learn advanced targeting, creative optimization, AI tools, and analytics to unlock success and boost ROI.
Nitin Mahajan
—
January 17, 2026
Fill out this form, and we will get in touch with you.
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 Parquet, and why is everyone talking about its "columnar advantage"? At its heart, Parquet is a way to store data files, and it does things a bit differently than older formats you might be used to, like CSV. Instead of lining up all the information for a single record (a row) together, Parquet stacks up all the data for a specific category (a column) together. Think of it like organizing your music collection: instead of putting all the songs by one artist on one shelf, you put all the 'rock' songs on one shelf, all the 'jazz' songs on another, and so on. This might sound odd at first, but it makes a huge difference when you're trying to find specific information quickly.
Most of us are familiar with row-based storage, probably from spreadsheets or basic databases. In this setup, each row is a complete record. If you have a table of customer data with columns like 'Name', 'Email', 'Address', and 'Purchase History', a row-based format stores all of that for one customer together. This is great if you always need to see everything about a single customer at once. However, what happens when you only need to know the total number of purchases across all customers? You'd still have to read through every single customer's entire record, picking out just that one piece of information. That's a lot of reading for data you don't even care about.
Parquet flips this. It stores all the 'Name' data together, then all the 'Email' data together, and so forth. This means if you only need to calculate the total number of purchases, you only read the 'Purchase History' column. All the names, emails, and addresses? They're left untouched on disk. This ability to ignore irrelevant data is the core of Parquet's efficiency.
This columnar approach directly impacts how fast you can get data out. When you ask for specific columns, the system only needs to access those particular data blocks. For example, if you're running an analysis on sales figures and only need the 'price' and 'quantity' columns from a massive sales table, Parquet can zoom straight to those columns. It doesn't have to wade through customer IDs, timestamps, or product descriptions that aren't relevant to your current task. This drastically reduces the amount of data that needs to be read from storage, which is often the slowest part of any data operation. It's like having a super-organized filing cabinet where all the 'sales' reports are in one drawer, and you can just pull that drawer out without disturbing the 'HR' or 'Marketing' files.
For analytical tasks, especially with big data, this is a game-changer. Analytics often involves looking at trends, calculating averages, or summing up values across millions or billions of records. These queries typically focus on a subset of columns, not entire rows. Parquet's design is perfectly suited for this. By storing data column by column, it allows for:
The way Parquet organizes 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. This layered organization is key to Parquet's ability to handle large volumes of data efficiently and speed up queries.
In short, if your work involves analyzing large datasets and you frequently query specific pieces of information, the columnar nature of Parquet is what makes it so incredibly fast and space-saving.
So, what makes Parquet stand out from the crowd? It's not just one thing, but a combination of smart design choices that make it so good at handling big data. Let's break down the main features.
Parquet really shines when it comes to squeezing data down. Because it stores data in columns, all the values in a single column are usually of the same type and often have similar patterns. This makes them prime candidates for compression. Think about it: a column of all 'true' or 'false' values will compress way better than a mixed bag of data. Parquet uses various algorithms, like Snappy or Gzip, and can even apply different compression methods to different columns based on their content. This leads to significantly smaller file sizes, which means less storage space needed and faster data transfer.
Beyond just compression, Parquet uses clever encoding methods to make data even more compact and quicker to read. Different encoding types are used depending on the data within a column. For example:
These techniques reduce the amount of data that needs to be read from disk, speeding up queries.
Modern data isn't always simple tables. Parquet is built to handle nested data structures like lists, maps, and structs without a fuss. It uses a record-shredding and assembly algorithm that can flatten these complex types into a columnar format. This means you don't have to pre-process or transform your data into a simpler format before storing it, which saves a lot of effort.
Data needs change. Sometimes you need to add a new column to your dataset, or maybe change the type of an existing one. Parquet is designed to handle this gracefully. You can add new columns to your schema without invalidating old data. When you read data, Parquet can handle cases where older files might not have a particular column that newer files do, or vice-versa. This flexibility is a big deal for keeping your data pipelines running smoothly as your requirements evolve.
Parquet's ability to adapt to schema changes without requiring a complete rewrite of existing data is a major advantage. It allows data systems to remain flexible and responsive to evolving analytical needs or new data sources, preventing costly and time-consuming data migrations.
So, why should you even bother with Parquet? Well, the advantages are pretty clear, especially when you're dealing with a lot of data for analysis. It's not just about saving a bit of space; it's about making your whole data process run smoother and faster.
This is a big one, especially with cloud storage bills. Because Parquet stores data by column, and all the data in a column is usually the same type, it can be compressed really well. Think about it: compressing a list of numbers is way easier than compressing a mixed bag of text, numbers, and dates all jumbled together. Parquet uses smart compression techniques, and when you combine that with its columnar layout, your data just takes up less room. This means you're paying less for storage, which adds up quickly when you're talking about terabytes or petabytes of information.
Imagine you have a huge table with a hundred columns, but your question only needs data from two of them. With older row-based formats, your system might have to read through all the data for every single row, even the stuff you don't need. Parquet flips this. It knows which columns are where, so it can just grab the data it needs and completely ignore the rest. This is often called "data skipping" or "predicate pushdown." It's like being able to pull out just the red marbles from a giant jar without having to empty the whole thing first. This makes queries run significantly faster, which means you get your answers quicker.
When you're processing massive amounts of data, every bit of efficiency counts. Since Parquet files are smaller due to compression and queries only read the necessary columns, your data processing jobs can move faster. Less data being read from disk and less data being written back means your systems can handle more work in the same amount of time. This boost in "throughput" is super important for big data pipelines, machine learning training, and any other operation where you're crunching large datasets regularly.
Parquet's design is all about making data storage and retrieval as efficient as possible for analytical tasks. It's built to handle large volumes of data and speed up the process of getting insights from it. While it might not be the best for constantly updating individual records, for reading and analyzing big datasets, it's a game-changer.
Here's a quick look at how Parquet stacks up in terms of efficiency:
Parquet isn't just a file format; it's become a building block for how we store and manage massive amounts of data today, especially in data lakes and the newer concept of lakehouses. Think of it as the sturdy foundation upon which these modern data platforms are built. Its columnar nature makes it super efficient for reading only the data you need, which is exactly what you want when you're sifting through terabytes or petabytes of information. This efficiency is why so many companies use Parquet to store their raw data in cloud storage like S3 or Azure Data Lake Storage.
The shift towards data lakes and lakehouses means we need formats that can handle diverse data types and evolving schemas without breaking everything. Parquet fits this bill nicely, offering a balance between performance and flexibility that older formats just couldn't match.
Parquet plays a big role in how we build these modern data systems. It's not just about storing files; it's about making those files usable for analytics. Tools like Spark, Presto, and Trino are built to work really well with Parquet, allowing data scientists and analysts to query data directly from storage without needing to move it into a separate database first. This makes the whole process faster and simpler.
When you're dealing with massive datasets, the ability to quickly access specific pieces of information is key. Parquet's columnar layout means that if your query only needs a few columns, the system can skip reading all the other columns entirely. This is a huge performance boost. It's like looking for a specific book in a library by only checking the shelves for that book's genre, instead of scanning every single book on every shelf.
Parquet is also the foundation for other advanced table formats that add even more capabilities. Formats like Delta Lake and Apache Iceberg build on top of Parquet files. They add features like transactional integrity (ACID properties), schema evolution management, and time travel (the ability to query older versions of your data). These formats essentially use Parquet as their storage engine but provide a more robust and manageable layer on top, making them ideal for building reliable data lakehouses.
These formats work by managing Parquet files along with metadata that tracks changes and versions, giving you more control and reliability over your data.
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 requires active configuration of its modular encryption framework and integration with a key-management system (KMS) to be secure.
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 envelope encryption model allows independent encryption of each column with distinct keys, enabling precise access control where analysts might decrypt only non-sensitive columns without exposing protected data. The architecture supports two cryptographic modes: AES-GCM for full data/metadata authentication and AES-GCM-CTR for lower-latency operations with partial integrity.
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 remain accessible. For example, you might encrypt a 'social_security_number' column with a highly restricted key, but allow broader access to a 'product_name' column. This granular control is a big step up from just encrypting an entire file, which can be overkill and hinder legitimate access for different teams. It's a big help when your data needs grow over time.
To really lock down sensitive data, you need to think about a few things:
While Parquet offers powerful tools for securing data, it's important to remember that the format itself doesn't magically make your data safe. You need to actively implement and manage these security features. Combine encrypted Parquet files with input validation, patch management, and access control to create a secure data pipeline. This approach is key for building secure analytics on large datasets.
It's also worth noting that a critical vulnerability (CVE-2025-30065) was identified in Apache Parquet's Java library, which could allow arbitrary code execution if malicious Parquet files are ingested. Mitigation involves patching to version 1.15.1, implementing input validation before ingestion, and using encryption protocols like AES-GCM.
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 a good question to ask, especially when you're dealing with a lot of information. Think of it like choosing the right tool for a job; you wouldn't use a hammer to screw in a bolt, right? Different formats are built for different tasks.
Row-based formats, like the ever-present CSV (Comma Separated Values) or Avro, store data one record at a time. Imagine a spreadsheet where each row is a complete entry. This is fine for simple data exchange or when you need to read or write entire records frequently. However, when you're doing analytics and only need a few columns from a massive table, row-based formats become a real pain. You end up reading a lot of data you don't actually need, which slows things down and wastes resources. Parquet, on the other hand, stores data by column. This means all the values for 'column A' are together, all values for 'column B' are together, and so on. This is a game-changer for analytical queries. If your query only asks for 'column A' and 'column C', Parquet can just read those specific columns, skipping all the others. This is often called 'column pruning' or 'predicate pushdown', and it's a big reason why Parquet is so much faster for analytics.
Here's a quick look:
When you're working with big data and analytical workloads, the difference between row-based and columnar storage can be the difference between waiting hours for a query and getting results in minutes. Parquet's design directly addresses this bottleneck.
Parquet isn't the only columnar format out there. ORC (Optimized Row Columnar) is another popular choice, especially within the Apache Hive ecosystem. Both Parquet and ORC are columnar, meaning they share many of the same advantages over row-based formats, like efficient compression and faster analytical query performance due to column pruning. They both support advanced encoding schemes and schema evolution. However, there are some subtle differences. Parquet is often cited as having slightly better compression ratios and potentially faster read performance in certain scenarios, partly due to its more granular control over encoding and its widespread adoption across various big data frameworks like Spark and Presto. ORC, while very capable, is sometimes seen as more tightly integrated with Hive. Ultimately, the choice between Parquet and ORC can depend on your specific ecosystem and performance needs, but Parquet has gained broader traction in many modern data lake architectures. You can find more details on how Parquet and ORC compare in terms of performance and features on various data format comparisons.
So, when should you definitely reach for Parquet? It really shines in a few key areas:
Basically, if your workload involves reading only a subset of columns from very large datasets, and performance or storage cost is a concern, Parquet is likely an excellent choice. It's designed to handle big data efficiently and plays well with most modern data processing tools.
So, we've looked at what makes Parquet work. 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 a Parquet file like a super-organized box for storing lots of information. Instead of putting all the details about one thing (like a person) all together, Parquet groups similar pieces of information together. So, all the ages go in one spot, all the names in another, and so on. This makes it way faster for computers to find and use just the information they need for tasks like making reports or analyzing trends.
Imagine you have a big list of people and you only want to know their heights. If the data is stored row by row, the computer has to look at each person's name, address, and other info before it can find their height. But if the data is stored column by column, the computer can just go straight to the 'height' column and grab all the heights super quickly. This saves a lot of time and effort, especially when you have tons of data.
Yes, it really does! Because all the data in a column is usually the same type (like all numbers or all words), it's much easier to squish it down using special methods called compression. It's like packing clothes really tightly in a suitcase. This means Parquet files take up much less room, which saves money on storage, especially when you're using cloud services.
Parquet is pretty flexible. If you need to add a new type of information to your data later on, like a new category or a new measurement, you can usually do that without having to reorganize all your old data. This makes it easier to keep your data system up-to-date as your needs change.
Absolutely! Parquet is designed to work well with many different data tools and platforms that are used for big data. This means you can often share data stored in Parquet between different systems without a lot of trouble, making it a versatile choice for data projects.
Parquet itself doesn't automatically make your data secure. Think of it like a strong box – it can hold things safely, but you still need to lock it. Parquet has features that allow you to add security, like encrypting specific parts of your data. You need to set up these security measures yourself to protect sensitive information.
