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Why Businesses Need Salesforce Parquet

Benefits of making Salesforce data available in Parquet format

Source: Image created by OpenAI’s DALL-E, February 2, 2024

Businesses that rely on Salesforce Customer 360 need better ways to efficiently manage and process their data. To get the most value from their data, many users export it into analytics and business intelligence systems. Depending on what file format they use, however, this can be challenging. Parquet makes it much easier and more cost-efficient.  

What is Parquet?

Parquet is an open-source, columnar storage file format designed for efficient data storage and retrieval. It stores all values of a single column together. This differs from row-wise storage, where each row’s data is stored sequentially.

Developed by Apache, Parquet is intended for complex nested data structures. It offers high data compression and encoding schemes. This makes it ideal for handling large datasets, and a popular format for data analysis and machine learning. 

Part of Parquet’s “magic” is that it stores each column’s values in contiguous memory locations. In other words, data from the same column are physically located close together. This enables Parquet to deliver the high efficiency and performance needed for big data processing and analytics.

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5 advantages of storing values in contiguous memory locations

Parquet provides faster data access, improved compression, reduced input/output (I/O) operations, better cache utilization, and efficient parallel processing. 

  1. Enhanced Read Efficiency
    When a query accesses a column, it can quickly read the column’s data in a sequential manner. This eliminates the overhead of skipping over irrelevant data from other columns.
  2. Improved Compression
    Since each column typically contains the same type of data (e.g., all integers, all dates), the data in each column tends to have similar patterns and ranges. This allows for more effective compression because compression algorithms perform better on data with less variability. More compression also enables more cost-effective storage.
  3. Selective Access and I/O Efficiency
    Analytical queries often require accessing a subset of columns. Parquet makes this possible by enabling selective access. It loads only the columns that are relevant to a query. This also reduces I/O overhead for reading data.  
  4. Optimized Cache  
    Modern CPUs are designed to prefetch data into cache lines. By storing data contiguously, Parquet increases the likelihood that required data is already in the CPU cache or can be loaded efficiently. This reduces cache misses and improves overall processing speed.
  5. Faster Data Processing
    Parquet makes it possible to process multiple values in parallel rather than sequentially. This can significantly speed up data processing.
Source: Image created by OpenAI’s DALL-E, February 2, 2024

How Parquet compares to CSV for Salesforce

CSV, which stands for “Comma-Separated Values,” is a row-based file format. People use it to store tabular data, such as a database or a spreadsheet. Data in a CSV file is represented by plain text, making it a straightforward way to represent structured data.

Its simplicity and wide compatibility make CSV ideal for quick data export and import with small to medium-sized data sets. However, it also makes CSV less suited for complex data structures and large datasets, where performance is critical.

Because Parquet stores data in columns, it provides better compression and faster read times for analytical queries. Parquet’s columnar storage also means it can handle Salesforce’s complex nested data more effectively. Moreover, unlike CSV, Parquet supports advanced data types and preserves data schema. This is crucial for maintaining data integrity over time.

How GRAX makes Salesforce data analysis easier with Parquet

With GRAX’s solution, it automatically writes the real-time and historical data it stores into Parquet form. We chose Parquet as the file format because of its strong compression and compatibility. 

GRAX leverages Parquet format so that businesses can effortlessly activate Salesforce data for AL/ML and analytics. Together, we make it easier for businesses to gain valuable insights into trends and make informed decisions.

How does GRAX do this?

Our complete data lifecycle management solution continuously collects and replicates every version of your Salesforce data changes. Equally important, it stores all this data in your organization’s own cloud instance, whether Amazon AWS, Microsoft Azure, or Google Cloud Platform – making it even easier to facilitate data access between your data lake/data warehouse and preferred data analysis tools. 

Parquet is well-supported by all major ETL, data storage, and data processing platforms. This means GRAX customers can easily pipe Salesforce data into a central data lake or data warehouse such as Snowflake. They can easily feed analytics platforms such as Tableau, Microsoft Azure Synapse Analytics, AWS QuickSight, or PowerBI.

Using Parquet with Salesforce data enables more efficient, cost-effective, and insightful data automation and management. By leveraging Parquet with platforms like GRAX, Salesforce users can unlock new levels of performance and insight.  

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