Big data has taken the world by storm, and as enterprises worldwide scramble to make sense of it, it continues to hit hard. Given the amounts of data produced daily, not only are they overwhelmed dealing with it, but they are also concerned that their existing ETL pipelines might not be able to cope without a solid data warehousing strategy.
Enter big data file formats, such as Avro, Feather, Parquet, ORC, etc. These evolved data file formats are incredibly efficient in storing big data. But then again, with multiple big data file formats available, chances are high that you’ll find yourself standing at a crossroads when deciding to use one of these data file formats to meet business requirements (think CSV vs. Avro vs. Parquet vs. ORC, etc.)
In this blog, we’ll discuss the two popular big data file formats, Avro and Parquet, to help you decide which option is more suitable for your data management requirements.
What is Apache Avro?
Developed as part of Apache’s Hadoop project, Avro is a data serialization and exchange system that supports various programming languages.
It’s a row-based data storage file format with an ability to pass data from applications written in different languages.
The language-neutral file format stores the schema in JSON, while the data itself is stored in a binary format, and both the data and the schema are stored in a single file or message to optimize the storage size.
What sets Avro apart from other data file formats is schema evolution, meaning that it supports dynamic schemas.
As far as the data types are concerned, Avro supports nested, complex (user-defined records, enumerations, arrays, unions, and maps), and primitive (string, Boolean, long, and int) data types.
What is Apache Parquet?
Parquet is a big data file format in the Hadoop ecosystem designed to handle data storage and retrieval efficiently. Like Avro, Parquet is also language agnostic, i.e., it is available in several programming languages like Python, C++, Java, and so on.
Parquet is a column-based file format. In contrast to how Avro stores the data and the schema, Parquet embeds the schema within the data instead of storing it in a different format.
Additionally, Parquet supports highly efficient encoding and compression schemes that significantly minimize the query run time, the amount of data that must be scanned, and the overall cost.
Now that we have introduced both data file formats, it’s time for the Parquet vs. Avro showdown.
Row vs Columnar Format
Both file formats are a great choice for your ETL pipelines. However, the decision to incorporate one or the other into your data integration ecosystem will be guided by your business requirements.
For instance, Avro is a better option if your requirements entail writing vast amounts of data without retrieving it frequently. This is because Avro is a row-based storage file format, and these types of file formats deliver the best performance with write-heavy transactional workloads.
On the other hand, if your use case requires you to analyze petabytes of data without sacrificing performance, then a column-based file format, such as Parquet, is your best bet since column-based file formats naturally deliver unparalleled performance with read-heavy analytical querying of data.
As far as ETL is concerned, you will find that Avro outperforms Parquet. This is because Parquet is designed for querying only subsets of specific columns instead of querying entire data from all the columns.
Let’s say you have an Avro file, and for some reason, you need to change its schema. What would you do? Simple! You’ll rewrite the file with a new schema.
Now let’s say there are terabytes of Avro files, and you need to change their schemas. What happens now? Will you rewrite all the files one by one every time you want to change the schema? Probably not!
This is where schema evolution comes in.
Schema evolution can be defined as a feature or the ability of a database management system that enables you to make changes in the current schema to cater to the data that changes over time. In simple terms, it enables you to modify the schema to accommodate new data while ensuring its backward compatibility with the old data’s schema.
Essentially, it allows you to read the entire data together as if there was a single schema. It is frequently used with the append and overwrite functions to automatically add new columns.
While column-oriented data file formats, including Parquet, also support schema evolution, modifying the schema in these data file formats is expensive. That’s because any change in the schema will require reprocessing of entire data sets as opposed to record-by-record in row-based data. Moreover, Parquet’s schema evolution is not as mature as Avro’s, as it only allows for an appending schema. In contrast, Avro is more sophisticated and is capable of handling changes in missing, altered, and new fields.
One of the main benefits of using big data file formats is a significant reduction in storage requirements and, consequently, costs. This benefit is achieved via multiple compression methods that both Avro and Parquet support. However, the difference lies in how the compression is performed and how efficient it is.
With Avro, you get the following compression types:
Compression methods supported by Parquet include:
In general, column-based storage file formats compress data more efficiently compared to their row-based counterparts.
So, if you’re looking to make the most of your ETL pipelines and target storage systems in terms of efficiency, Parquet is a better choice.
Which one should you choose?
Although Avro and Parquet cater to very different ETL use cases and applications, it is the business requirement that will point to the type of data file format you should use. Having said that, let’s not forget that it doesn’t always have to be Avro vs Parquet. You can use both file formats and have the best of both worlds.
Convert Avro to Parquet using Astera Centerprise
Astera Centerprise is a no-code data integration platform powered by a parallel processing ETL/ELT engine that streamlines all your data integration projects.
Let’s say you want to make the best use of your data storage capacity. However, you realize that most of your data is stored in .avro, presenting you with the opportunity to convert it to .parquet.
Now, there can be two ways you can go about it: a) write extensive code and manually convert all the files to .parquet, or b) build a reusable pipeline using Astera Centerprise to convert all your files without writing a single line of code.
With Astera Centerprise, you also get the flexibility to choose from a range of compression methods for both Avro and Parquet, enabling you to strike the perfect balance between performance and efficiency. For Avro, these include:
- Null: applies no compression
- Deflate: provides the highest compression rate
- Snappy: delivers reasonable compression at higher speeds
- Zstandard: delivers higher compression without taking a toll on CPU usage
And for Parquet, you can choose between:
- Snappy: fair amount of compression at high speeds
- Gzip: provides higher compression ratios but requires more CPU resources
Setting up the source and destination
To build the pipeline, simply drag and drop the Avro File Source onto the dataflow designer and provide the source file path. On the next screen, you’ll see the Output Dataframe Layout Builder where you’ll be able to view data types, formats, description, and more.
Although the image above only shows a single data type. You can use the following data types for Avro with Astera Centerprise:
- Complex data types
- Logical data types
- Primitive data types
You can now head over to the Config Parameters window and define and configure dynamic parameters if needed; otherwise, you can skip ahead and configure the Parquet File Destination.
Just like before, drag and drop the Parquet File Destination, provide the destination file path, and select your desired compression method.
Next, you’ll see the familiar Layout Builder screen, where you can make changes if necessary. Finally, you’ll land on the Config Parameters screen, where you can define dynamic parameters if needed.
Improving data quality
Once you’ve configured both the source and the destination, you’re ready to convert Avro to Parquet. Next, you’ll want to ensure that your pipeline also eliminates any issues in data quality.
For that, you can apply certain data quality rules and filter out the data that you deem irrelevant or no longer needed using the following transformations:
- Data Quality Rules
Next, simply map the fields via point-and-click, and your pipeline to convert Avro to Parquet is ready.
Automating the pipeline
You’ve created the pipeline. Now, take the final step: use automation and workflow orchestration to set your pipeline to run automatically whenever it receives a trigger or a certain event occurs.
For example, you can set it to run automatically whenever new .avro files are dropped in a specific folder. Or you can also set it up so that it runs automatically on, let’s say, every Monday at 1 PM.
How much storage space can you expect to recover?
To provide an estimate of the amount of storage space that you can expect to save by converting .avro files to .parquet, we took an .avro source file with 25 million records and converted it into .parquet using Astera Centerprise. This is what the pipeline looks like:
The size of this .avro file was 2,757,965 KB or approximately 2.63 GB. To achieve maximum efficiency, we used Gzip as the compression method, and the resulting .parquet file size was 865,203 KB or around 0.83 GB. Consequently, we were able to save roughly 69% of storage space simply by changing file formats.
Why use Astera Centerprise?
Besides converting from one format to another, Astera’s native connectors also enable you to ETL data from various sources, including databases, applications, and file formats, and store it in .avro and .parquet file formats effortlessly.
To recap, Astera Centerprise empowers you to:
- Leverage big data file formats to handle huge amounts of data seamlessly
- Build end-to-end ETL/ELT pipelines in a no-code, drag-and-drop environment
- Streamline data integration using automation and process orchestration
- Connect to data sources and destinations of your choice with a library of native connectors
- Improve data health using built-in data quality features
- Accelerate data integration with point-and-click data mapping
Ready to streamline big data integration for your business? Try Astera Centerprise with a 14-day free trial. If you’d like to discuss your business use case with our data experts, call +1 888-77-ASTERA today!