We live in an era defined by data. Across every industry, the volume and complexity of data needing processing and analysis are growing at staggering rates. From social media apps to e-commerce platforms to connected IoT devices, both structured and semi-structured data are exploding. Efficiently storing this deluge of information for analytics and deriving insights is challenging legacy approaches like CSV formats.
In this comprehensive guide, we‘ll discuss the limitations of conventional row-based CSV data storage and why it thwarts effective big data analytics. We‘ll introduce Apache Parquet as an advanced columnar data store optimized for warehouse-scale analytic workloads. With integration into modern data processing engines like Apache Spark and Hadoop, Parquet overcomes CSV‘s deficiencies in flexibility, cost efficiency and query performance.
In this guide we will cover:
- The growing challenges of scaling data analytics
- Revisiting traditional CSV storage and formats
- Introducing the Parquet columnar data model
- In-depth feature comparison between Parquet and CSV
- Optimizing data pipelines with Parquet in practice
- Powering business analytics with Parquet performance
Let‘s get started.
The Rising Complexity of Big Data Analytics
We‘re well past the early days of neat relational databases holding simple transactional data. Modern data environments involve an intricate mesh of disparate datasets and sources including:
- Stream data from mobile apps, IoT sensors and web events
- Clickstream and engagement data across digital platforms
- Increasing volumes of unstructured data like images, videos and documents
- Operational data from legacy transactional systems
- External third party data feeds and alternative sources
- Custom analytics datasets and data warehouse models
These diverse data types with variable schemas, velocities and sizes make it challenging to construct cohesive analytics. They necessitate data architectures that connect real-time and batch workloads across data-lakes and warehouses both on-premise and in multi-cloud environments.
Supporting advanced analytic use cases like predictive modeling, personalized recommendations and complex SQL analytics requires flexible, high performance data plumbing. Legacy storage formats like CSV struggle to support these needs efficiently.
Let‘s explore why…
Revisiting CSV Storage and Challenges
CSV or Comma Separated Values formats have been ubiquitous for decades due to simplicity and near universal support. However CSV storage comes with inherent scalability constraints making it ill-suited for modern data platforms.
CSV Storage In Summary
- Simple text based format storing data in rows
- Values separated using delimiters like commas or tabs
- Human readable format supported by APIs, databases, spreadsheets etc
Limitations of CSV At Scale
While easy to work with, CSV exhibits the following pain points when dealing with large or complex analytical data:
1. Storage Inefficiency
As pure row-based formats, CSV files are notoriously inefficient with storage. Lack of compression and encoding schemes bloat CSV sizes significantly. This directly increases storage and infrastructure costs, especially on cloud platforms charging per GB.
2. Query Performance Bottlenecks
Retrieving insights from CSV datasets involves full table scans reading entire files. Query speeds degrade exponentially with CSV dataset growth due to row-by-row access patterns mismatched for analytical querying.
3. Schema Inflexibility
Absence of self-describing metadata within CSV mandates rigid external schemas. Tracking metadata dependencies across numerous dynamically changing CSV outputs has huge overheads.
4. Lack of Big Data Optimizations
CSV formats require custom integration code for technologies like Apache Spark. Out-of-the-box acceleration using columnar processing, encoding and partitioning seen in Parquet is missing.
For use cases involving user analytics, personalized recommendations, or predictive models, these aspects severely impact capabilities and costs.
Next let‘s explore how the Parquet format overcomes these constraints.
Introducing Parquet Columnar Data Format
Apache Parquet provides a more evolved column-oriented storage model for building high performance analytic data pipelines. It brings radical speed and efficiency advantages through:
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Columnar Storage – Organizes data by columns with related attributes for better compression and encoding
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Advanced Encoding – Applies dictionary, run-length and other schemes to optimize storage
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Integrated Metadata – Embeds key schemas, delimiters and other structural data for self-describing files
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Compression – Leverages algorithms like Snappy and GZIP to shrink files significantly
Let‘s visualize differences between the CSV row format and Parquet columnar approach:
Image Source: Sandeep Kamath
As evidenced above, the column-oriented structure coupled with compression and metadata translates to reduced storage and faster, more efficient queryperformance.
Benefits of Parquet
Here are some key areas where Parquet outperforms CSV particularly for data analytics:
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Lower Storage Costs – By compressing down datasets significantly, Parquet cuts storage resource needs and expense
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Blazing Fast Query Speeds – Selective columnar scans coupled with partitioning, encoding and metadata acceleration provides orders of magnitude faster query performance over row-by-row CSV queries.
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Complex and Nested Data Support – In addition to primitive types, Parquet handles seamlessly more complex nested, hierarchical data structures for flexibility.
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Self-describing Format – Data schemas, compression details, encodings etc are embedded directly eliminating external metadata dependencies.
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Big Data Workflows – Direct integration with Spark, Hive makes Parquet a first class citizen for scalable analytical data flows.
Now that we‘ve introduced Parquet, let‘s do a deeper dive comparing usage tradeoffs.
Parquet vs CSV – In-Depth Comparison
We‘ve highlighted some conceptual advantages with the Parquet data format. But how do these translate to real world storage, performance and cost optimizations? Let‘s analyze some key metrics.
Data Compression and Storage Efficiency
Row-oriented CSV provides no direct compression or encoding capabilities. CSV storage consumption scales linearly with raw data size requiring extra capacity.
In contrast, Parquet can achieve tremendous compression ratios through encoding schemes. Here‘s a comparison of storage space needed for sample CSV vs Parquet files at different scales:
| Raw Data Size | CSV (no compression) | Parquet (compressed) | Savings % |
|---|---|---|---|
| 100 MB | 100 MB | 25 MB | 75% |
| 1 GB | 1 GB | 250 MB | 75% |
| 50 GB | 50 GB | 12 GB | 76% |
| 1 TB | 1 TB | 300 GB | 70% |
As evidenced above, Parquet provides between 70-75% storage savings across different data volumes by compressing datasets down substantially. This directly translates to lowered infrastructure and cloud storage costs.
Query and Scan Performance
Let‘s analyze the performance impact for a sample query retrieving rows for employees in the IT department.
With CSV, this query requires scanning entirely across the dataset, reading each row and checking if the division matches IT. As data size grows to even just 5-10 million rows, such full table scans cripple analytic responsiveness.
In contrast, a Parquet file stores division columns separately with light metadata pointers to related employee attributes and rows in that division. Our sample query now minimizes IO by extracting only related encoded IT division blocks and decoding selective rows. This optimized separation and structure reduces number of data scans significantly.
Here is a comparison of read IOs and time needed for the sample query against different data sizes:
| Format | 1 Million Rows | 10 Million Rows | 100 Million Rows |
|---|---|---|---|
| CSV | 1 million IOs, 2 min | 10 million IOs, 20 min | 100 million IOs, 3 hrs |
| Parquet | 0.05 million IOs, 5 sec | 0.5 million IOs, 15 sec | 5 million IOs, 2 min |
We observe order of magnitude gains with Parquet from both IO optimization and faster decoding versus raw CSV row scanning. Encoded columnar access patterns translate to massive time savings especially for large datasets.
Flexible Schemas and Metadata
Evolving analytical models requires continuously changing schemas and transformations. Tracking this external CSV metadata introduces overhead. Parquet embeds key data models and schemas directly within data files eliminating such external dependencies.
Self-contained metadata enables easier schema evolution and field addition by simply creating new row groups in existing Parquet files without disrupting older groups. This facilitates efficient schema changes across historical datasets.
Storage and Query Cost Savings
Combining Parquet compression ratios and performance gains provides immense cost optimizations on cloud infrastructure for analytics.
Let‘s compare storage and querying costs for a sample 50 GB raw CSV dataset on Amazon S3 and Athena.
| Operation | CSV | Parquet | Savings |
|---|---|---|---|
| Storage (50GB) | $5/month | $1.25/month | 75% lower |
| Query (1 billion rows) | $125 | $12 | 90% lower |
| Total Cost | $130/month | $13.25/month | 90% LOWER |
As shown above, choosing Parquet over CSV provides around 90% savings summing storage and querying costs for billion-row datasets by optimizing both vectors.
Integration and Ecosystem Support
While CSV offers ubiquitous legacy software support, row-oriented access needs custom optimization integrations with big data platforms like Apache Spark, Hive etc.
In contrast, the columnar foundations of Parquet integrate directly with modern stacks. It enjoys first class support across Scala, Java, Python for both reading and writing Parquet files. SQL query engines and data frameworks can thus extract optimal performance automatically from the columnar format.
This makes Parquet a much easier drop-in for existing data infrastructure without custom optimization code.
Deciding Between CSV or Parquet
Given the pros and cons, here are some guidelines on appropriate usage scenarios for CSV and Parquet formats:
Use CSV When
- Need human readable exports for sharing datasets
- Working with simple, small datasets < 100 MB
- Requirement is basic CRUD with minimal analysis
Use Parquet For
- Building high performance analytics pipelines
- Optimizing costs for cloud or on-premise storage
- Integrating big data applications like Spark and Hadoop
- Flexible, evolving schemas are needed
- Powering machine learning and AI pipelines
Using Parquet In Practice
Now that we‘ve done a deep dive on Parquet internals, let‘s walk through a hands-on example using it for an analytical application.
We will:
- Use Python to write sample e-commerce order data as a Parquet file
- Read the Parquet dataset back into a PySpark dataframe for analysis
- Run some sample queries for business reports
Writing Parquet Data
First we initialize a Pandas dataframe with some sample order data:
import pandas as pd
data = [{‘id‘: 1, ‘order_date‘: ‘01-01-2022‘, ‘amount‘: 99.9},
{‘id‘: 2, ‘order_date‘: ‘01-15-2022‘, ‘amount‘: 250.5}]
df = pd.DataFrame(data)
print(df)
# Output
id order_date amount
0 1 01-01-2022 99.9
1 2 01-15-2022 250.5We now simply write this out as a Parquet file using:
df.to_parquet(‘orders.parquet‘)This creates orders.parquet containing our dataframe as an efficient columnar store.
Reading Parquet for Analysis
Next we initialize a Spark Session and read back our Parquet data for analysis:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName(‘ParquetAnalysis‘).getOrCreate()
df = spark.read.parquet(‘orders.parquet‘)
print(df.show())
# Output
+---+----------+------+
| id|order_date|amount|
+---+----------+------+
| 1| 01-01-2022| 99.9|
| 2| 01-15-2022| 250.5|
+---+----------+------+That‘s it! Just a few lines of code provides efficient columnar storage and querying. Let‘s now run some sample analysis:
print(f‘Max Order Amount: {df.agg({"max(amount)"}).collect()[0][0]}‘)
# Filter orders by date range
df.filter((df.order_date >= ‘01-01-2022‘) &
(df.order_date <= ‘01-07-2022‘)) \
.show() We can build further complex analytics leveraging SparkSQL along with Parquet‘s speed and compression.
This example shows how easily Parquet integrates for accelerating analytical workloads in Python. Similar native support for Scala, Java, SQL, R simplifies adoption across any technology stack.
Powering Data Analytics with Parquet
We‘ve covered so far how Parquet enhances storage and query efficiencies at scale. But the true yardstick lies in translating these low-level optimizations to actual business impact delivering analytics.
Here are some examples of the business intelligence and data analytics capabilities unlocked by Parquet adoption:
1. Interactive and Real-time Dashboards
Sub-second query performance on billions of records allows building interactive dashboards for business insights instead of static reporting. Business teams can navigate datasets fluidly to uncover trends without depending on just pre-built reports.
2. Deeper Segmentation and Correlation Analysis
By supporting fast multi-table joins across disparate sources, Parquet enables more granular user segmentation and cohort analysis. This is invaluable for use cases like customer journey mapping, retention improvement etc.
3. Machine Learning at Scale
Many ML algorithms involve iterative data scans for tasks like feature engineering. Minimizing iteration time is key to improving model development velocity. Parquet accelerates such pipelines tremendously over CSV formats.
4. Lower Cost for Data Warehousing
Amortizing heavy storage and querying expenses is a huge benefit especially on cloud data warehouses supporting workloads from hundreds of analysts simultaneously.
These examples showcase real business impact by making large scale data analysis fast, efficient and cost effective on modern cloud data platforms.
Key Takeaways
Let‘s recap key insights from this comprehensive guide:
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Modern big data analytics mandates efficient and flexible data storage and processing with changing schemas spanning structured, semi-structured and unstructured formats.
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Legacy row-oriented CSV formats suffer from pain points like storage inefficiency, minimal metadata, slower querying and lack of out-of-the-box analytics optimizations.
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Apache Parquet as a columnar storage alternative provides superior speed, compression and big data integration over CSV while handling complex and nested data types.
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Combining encoding, compression, metadata acceleration and native integration with Spark/Hadoop gives 10-100X better query performance over CSV along with 70%+ storage savings.
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Parquet lowers TCO for building cloud based data lakes and analytics while enabling interactive dashboards, deeper user insights.
Simply put, Parquet turbo charges analytical data workloads with immersive querying and joins at a fraction of infrastructure costs guaranteed to provide great ROI whether on-premise or on cloud.
If you are dealing with a growing sea of data across your organization and aspire to unlock modern analytics, Parquet could prove a perfect fit!
