Parquet File Format - Explained to a 5 Year Old!

Summary

In this informative video, Nicola from Data Mozart explores the Parquet and Delta file formats, which have become de facto standards for data storage due to their efficiency and versatility. Parquet offers data compression, reduced memory consumption, and fast read operations, making it ideal for analytical workloads. Nicola explains the benefits of columnar storage and introduces the concept of row groups for optimizing query performance. Additionally, the video delves into Delta Lake, an enhancement of Parquet that supports versioning and ACID-compliant transactions, making it a powerful tool for data manipulation and analysis.

Takeaways

• 📈 Parquet and Delta file formats are becoming the de facto standard for data storage due to their efficiency in handling large amounts of data.
• 🌐 Traditional relational databases are being supplemented with Parquet for scenarios requiring analysis over raw data, such as social media sentiment analysis and multimedia files.
• 🛠️ The challenge of maintaining structured data without complex ETL operations is addressed by Parquet's design, which is both efficient and user-friendly for data professionals proficient in Python or SQL.
• 🔑 Parquet's five main advantages include data compression, reduced memory consumption, fast data read operations, language agnosticism, and support for complex data types.
• 🔄 The column-based storage of Parquet allows for more efficient analytical queries by enabling the engine to scan only the necessary columns, rather than every row and column.
• 📚 Parquet introduces the concept of 'row groups' to further optimize storage and query performance by allowing the engine to skip entire groups of rows during query processing.
• 📏 The metadata contained within Parquet files, including minimum and maximum values, aids the query engine in deciding which row groups to scan or skip, thus enhancing performance.
• 🧩 Parquet's compression algorithms, such as dictionary encoding and run-length encoding with bit-packing, significantly reduce the memory footprint of stored data.
• 🚀 Delta Lake format is described as 'Parquet on steroids', offering versioning of Parquet files and transaction logs for changes, making it ACID-compliant for data manipulation.
• 🔄 Delta Lake supports advanced features like time travel, rollbacks, and audit trails, providing a robust framework for data management on top of the Parquet format.
• 🌟 The combination of Parquet's efficient storage and fast query processing with Delta Lake's advanced data management features positions them as leading solutions in the current data landscape.

Q & A

What is the main topic of Nicola's video from Data Mozart?

The main topic of the video is the Parquet and Delta file formats, which have become a de facto standard for storing data due to their efficiency and features.

Why has the traditional relational database approach become less optimal for storing data?

The traditional relational database approach is less optimal because it requires significant effort and time to store and analyze raw data, such as social media sentiment analysis, audio, and video files, which are not well-suited to a structured relational format.

What is one of the challenges organizations face with traditional data storage methods?

One of the challenges is the need for complex and time-consuming ETL operations to move data into an enterprise data warehouse, which is not efficient for modern data analysis needs.

What are the five main reasons why Parquet is considered a de facto standard for storing data?

The five main reasons are data compression, reduced memory consumption, fast data read operations, language agnosticism, and support for complex data types.

How does the column-based storage in Parquet differ from row-based storage?

In column-based storage, each column is stored as a separate entity, allowing the engine to scan only the necessary columns for a query, thus improving performance and reducing the need to scan unnecessary data.

What is the significance of row groups in the Parquet file format?

Row groups in Parquet are an additional structure that helps optimize storage and query performance by allowing the engine to skip scanning entire groups of rows that do not meet the query criteria.

How does the metadata in a Parquet file help improve query performance?

The metadata in a Parquet file, which includes information like minimum and maximum values in specific columns, helps the engine decide which row groups to skip or scan, thus optimizing query performance.

What is the recommended size for individual Parquet files according to Microsoft Azure Synapse Analytics?

Microsoft Azure Synapse Analytics recommends that individual Parquet files should be at least a few hundred megabytes in size for optimal performance.

What are the two main encoding types that enable Parquet to compress data and save space?

The two main encoding types are dictionary encoding, which creates a dictionary of distinct values and replaces them with index values, and run-length encoding with bit-packing, which is useful for data with many repeating values.

What is Delta Lake format, and how does it enhance the Parquet format?

Delta Lake format is an enhancement of the Parquet format that includes versioning of files and a transaction log, making it ACID-compliant for operations like insert, update, and delete, and enabling features like time travel and audit trails.

What are the key benefits of using the Parquet file format in the current data landscape?

The key benefits of using Parquet include reduced memory footprint through various compression algorithms, fast query processing by skipping unnecessary data scanning, and support for complex data types and language agnosticism.

Outlines

00:00

📚 Introduction to Parquet and Delta File Formats

Nicola from Data Mozart introduces the Parquet and Delta file formats, highlighting their significance as de facto standards for data storage in the era of exponential data growth. The video aims to explore the advantages of these formats over traditional relational databases, especially for handling unstructured data like social media and multimedia files. Nicola emphasizes the importance of efficient data storage solutions that cater to the diverse skillsets of data professionals, proficient in either Python or SQL, and mentions the Apache Parquet format, which has been a game-changer since 2013.

05:01

🔍 Understanding Parquet's Columnar Storage Efficiency

This paragraph delves into the columnar storage approach of Parquet, contrasting it with row-based storage. Nicola explains how columnar storage allows for faster data read operations by scanning only the necessary columns for a query, thus improving performance in analytical workloads. The introduction of 'row groups' in Parquet is highlighted as a key structural feature that optimizes data processing. The paragraph also discusses the importance of metadata in Parquet files for enabling efficient data querying and the benefits of merging smaller files into larger ones for better performance. Compression techniques like dictionary encoding and run-length encoding are mentioned as methods to reduce memory footprint.

10:01

🚀 Delta Lake: Enhancing Parquet with Versioning and Transactions

Nicola introduces Delta Lake as an enhancement to the Parquet format, likening it to 'Parquet on steroids.' Delta Lake incorporates versioning of files and maintains a transaction log, making it ACID-compliant and capable of handling complex data manipulations such as inserts, updates, and deletes. The format supports time-traveling and rollbacks, offering a robust solution for data warehousing on a data lake, thus bridging the gap between traditional data warehouses and the flexibility of data lakes. The video concludes by emphasizing the efficiency of Parquet for memory consumption and fast query processing, solidifying its position as a leading storage option in modern data landscapes.

Keywords

💡Parquet

Parquet is a columnar storage file format that is highly efficient for analytic workloads. It is designed to optimize storage and query performance by compressing data and allowing for fast reads. In the video, Parquet is highlighted as a de facto standard for data storage due to its various benefits such as data compression, reduced memory consumption, and support for complex data types.

💡Delta Lake

Delta Lake is an open-source storage layer that brings reliability and structure to data lakes by adding features like ACID transactions, schema enforcement, and time-travel queries to the Parquet format. It is referred to in the script as 'Parquet on steroids', emphasizing its enhanced capabilities over the standard Parquet format, including versioning of Parquet files and transaction logs.

💡Data Compression

Data compression is a method of reducing the size of data to save storage space and improve transmission efficiency. In the context of the video, Parquet uses various compression algorithms to achieve reduced memory footprint, which is crucial for analytic workloads. The script mentions dictionary encoding and run-length encoding as two main encoding types that Parquet uses for data compression.

💡Row-based Storage

Row-based storage is a method of storing data where each row is stored as a sequence of values. The video script contrasts this with column-based storage, explaining that in row-based storage, the engine must scan every row to answer queries, which can be inefficient. An example from the script is how a query for 'how many users from the USA bought t-shirts' would require scanning all rows and columns, even though only specific columns are needed.

💡Column-based Storage

Column-based storage is a method where data is stored column-wise, allowing for more efficient querying, especially in analytical scenarios. The video explains that in column-based storage, only the necessary columns required by a query are scanned, which can significantly improve performance. Parquet, as a columnar format, leverages this approach to enhance data retrieval.

💡Row Group

A row group is a structure within the Parquet file format that groups together multiple rows of data. The script explains that row groups are used to further optimize the storage and querying process by allowing the engine to skip scanning entire groups of rows that do not meet the query criteria.

💡Metadata

Metadata in the context of the video refers to the data about the data within a Parquet file, such as minimum and maximum values in a column within a specific row group. This metadata is crucial for the engine to determine which row groups to skip or scan, thus optimizing query performance.

💡Projection

In the video, projection is related to the concept of a SELECT statement in SQL, determining which columns are needed by a query. It is an important aspect of query optimization in column-based storage formats like Parquet, as it allows the engine to skip scanning unnecessary columns.

💡Predicates

Predicates refer to the criteria defined in a query's WHERE clause, which rows must satisfy. The video script mentions predicates as a concept in query optimization, where the engine can skip scanning row groups that do not meet the query's conditions, thus improving performance.

💡ACID Transactions

ACID stands for Atomicity, Consistency, Isolation, and Durability, which are properties of database transactions intended to ensure reliability. In the context of the video, Delta Lake supports ACID transactions, allowing for operations like insert, update, and delete on Parquet files, thus providing a robust framework for data manipulation.

💡Data Lake

A data lake is a system or storage repository that holds a vast amount of raw data in its native format until it is needed. The video script mentions the concept of a 'data lake house', which is the idea of bringing the benefits of a data warehouse to a data lake environment, as provided by Delta Lake.

Highlights

Parquet and Delta file formats have become a de facto standard for storing data due to their efficiency in handling large amounts of data.

Traditional relational databases are no longer the only way to store and analyze data, with organizations now performing analysis over raw data formats like social media and audio/video files.

Parquet files offer reduced memory consumption and columnar storage, which is crucial for analytic workloads.

Parquet supports fast data read operations, which is essential for quick data analysis.

The language-agnostic nature of Parquet allows developers to use various programming languages to manipulate data.

Parquet is an open-source format, meaning there is no vendor lock-in.

Parquet supports complex data types and is a column-based storage format.

The difference between row-based and column-based storage is significant for analytical queries, with column-based storage being more efficient.

Parquet introduces the concept of row groups, which further optimizes storage and query performance.

Row groups in Parquet allow for more efficient data scanning by skipping unnecessary columns and rows.

Parquet files contain metadata that helps the engine decide which row groups to skip or scan, improving query performance.

Merging multiple smaller Parquet files into one larger file can enhance query performance by reducing metadata reads.

Data compression in Parquet is achieved through dictionary encoding and run-length encoding with bit-packing, significantly reducing file size.

Delta Lake is described as Parquet on steroids, offering versioning of Parquet files and transaction logs for change tracking.

Delta Lake supports ACID-compliant transactions, enabling operations like insert, update, delete, and rollbacks.

Delta Lake can be thought of as a data warehouse on the data lake, combining the benefits of structured storage with the flexibility of data lakes.

Parquet's efficiency in memory consumption and fast query processing makes it a top choice for data storage in the current data landscape.

Transcripts

hey my dear data friends it's Nicola

from data Mozart today in this video I

will show you all the ins and outs of

the parket and Delta file format which

let's be honest became a de facto

standard today for storing the data so

stay

[Music]

tuned so what's the deal with par and

Delta formats with amounts of data

growing exponential in recent time one

of the biggest challenges became finding

the most optimal way to store various

data flavors unlike in the not so far

past uh when relational databases were

considered the only way to go

organizations now want to perform

analysis over raw data think of social

media sentiment analysis audio video

files and so on which usually couldn't

be stored in traditional relational way

uh or storing them in traditional way

would require significant effort in in

time which increased the overall time

for analysis another challenge was to

somehow stick with a traditional

approach to have data stored in a

structured way but without the necessity

of uh designing complex and timec

consuming ETL uh operations ETL

workloads to move this data into the

Enterprise data warehouse additionally

what it half of data Professionals in

your organization are proficient with

let's say python which is typical for

data scientists and data engineers and

the other half like data analysts with

SQL put you insist that

pythonistas Versa or would you prefer a

storage option that can play to the

strength of your entire uh data team I

have good news for you something like

this already exists since 2013 and it's

called Apachi par before I show you ins

and outs of the parket file format

there are at least five main reasons why

par is considered a de facto standard

for storing data

nowadays data compression by applying

various encoding and compression

algorithms parquet file provides reduced

memory

consumption Coler storage this is of

Paramount importance in analytic

workloads their fast data read operation

is the key requirement but more on that

later in this video language agnostic as

already mentioned previously developers

may use different programming languages

to manipulate the data in the parquet

file open source format meaning you are

not locked with a specific wendor and

finally support for complex data

types we've already mentioned that

parquet is a column based storage format

however to understand the benefits of

using the parquet file format we first

need to draw the line between the row

based and column based V of storing the

data in traditional row based storage

the data is stored as a sequence of rows

something like this you may screen on

your screen now now when we are talking

about analytical scenarios some of the

common questions that your users may ask

are how many balls did we sell how many

user from the USA bought t-shirt what is

the total amount spent by customer Maria

Adams how many sales did we have on

January 2nd to be able ble to answer any

of these questions the engine must scan

each and every Row from the beginning to

the very end so to answer the question

how many users from the USA both t-shirt

the engine has to do something like

this essentially we just need the

information from two columns product for

t-shirts and Country for the USA but the

engine will scan all five columns this

is not the most efficient solution I

think think we can agree on that let's

now examine how the column store works

as you may assume the approach is quite

different in this case each column is a

separate entity meaning each column is

physically separated from other columns

going back to our previous business

question the engine can now scan only

those columns that are needed by the

query which are product and country

while skipping scanning the unnecessary

columns and in most cases this should

improve the performance of the

analytical

queries okay that's nice but the column

store existed before parquet and it

still exists outside of parket as well

so what is so special about the parket

format parket is a columnar format that

stores the data in row groups wait what

wasn't it enough complicated even before

this don't worry it's much easier than

it sounds let's go back to our previous

example and depict how parquet will

store the same chunk of data let's stop

for a moment and explain the

illustration you see as this is exactly

the structure of the parquet file I've

intentionally omitted some additional

things but we will come soon to explain

that as well columns are still stored as

separate units but parquet introduces an

additional structure called row group

why is this additional structure so

important you'll need to wait for an

answer for a bit

in online analytical processing

scenarios we are mainly concerned with

two concepts projection and

predicates projection refers to a select

statement in SQL language which columns

are needed by the query back to our

previous example we need only the

product and Country columns so the

engine can skip scanning the remaining

ones predicates refer to the ver clause

in SQL language which rows satisfy

criteria defined in the query

in our case we are interested in

t-shirts only so the engine can

completely skip scanning row group two

where all the values in the product

column equal socks let's quickly stop

here as I want you to realize the

difference between various types of

storage in terms of the work that needs

to be performed by the engine roll store

the engine needs to scan all five

columns and all six rows column store

the engine needs to scan two columns and

all six rows column store with row

groups the engine needs to scan two

columns and four

rows obviously this is an oversimplified

example with only six rows and five

columns where you will definitely not

see any difference in performance

between these three storage options

however in real life when you're dealing

with much larger amounts of data the

difference becomes more

evident now the fair question would be

how parket knows which row group to skip

or

scan parquet file contains

metadata this means every parquet file

contains data about data information

such as minimum and maximum values in

the specific column within the certain

row group furthermore every parquet file

contains a footer which keeps the

information about the format version

schema information column metadata and

so on I'll give you one performance tip

in order to optimize the performance and

eliminate unnecessary data structures

such as row groups and columns the

engine first needs to get familiar with

the data so it first reads the metadata

it's not a slow operation but it still

requires a certain amount of time

therefore if you're quering the data

from multiple small parquet files query

performance can degrade because the

engine will have to read metadata from

each file so you should be better off

merging multiple smaller files into one

bigger file but still not too big I hear

you I hear you Nicola what is small and

what is Big unfortunately there is no

single golden number here but for

example Microsoft Azure synapse

analytics recommends that the individual

parquet file should be at least a few

hundred megabytes in size can it be

better than this yes with data

compression so we've EXP explained how

skipping the scan of the unnecessary

data structures May benefit your queries

and increase the overall performance but

it's not only about that remember when I

told you at the very beginning that one

of the main advantages of the parquet

format is the reduced memory footprint

of the file this is achieved by applying

various compression

algorithms there are two main encoding

types that enable parquet to compress

the data and Achieve astonishing Savings

in

space dictionary encoding parquet

creates a dictionary of the distinct

values in the column and afterward

replaces real values with index values

from the dictionary going back to our

example this process looks something

like this and you might think why this

overhead when product names are quite

short right okay but now imagine that

you stored a detailed description of the

product such as long arm T-shirt with

application on the neck and and now

imagine that you have this product sold

million times yeah instead of having

million times repeating value long arm

blah blah parquet will store only the

index value integer instead of text run

length encoding with bid packing when

your data contains many repeating values

run length encoding or R abbreviated

algorithm may bring additional memory

savings can it be better than this yes

with a Delta l L file format okay what

the heck is now Delta Lake format to put

it in plain English Delta lake is

nothing else but the parquet format on

steroids when I say steroids the main

one is the versioning of parquet files

it also stores a transaction log to

enable keeping the track of all changes

applied to the parquet file this is also

known as acid compliant

transactions since it supports not only

asset transactions but also time

traveling like roll backs audit trails

and so on and data manipulation language

statements such as insert update and

delete you won't be wrong if you think

of the Delta Lake as a data warehouse on

the data lake or data lake house to

conclude parquet file format is one of

the most efficient storage options in

the current data landscape since it

provides multiple benefits both in terms

of memory consumption by leveraging

various compression algorithms items and

fast query processing by enabling the

engine to skip scanning unnecessary data

That's all folks if you enjoy this video

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you soon

[Music]

again