Text. JSON. XML. CSV. ORC. Sequence. Avro. Parquet…
So many choices. How do you choose the best file format for your business? You understand how important file formats are when saving important data; they can tremendously impact your projects in regards to space requirements and performance. In making a decision on which data format to use, it is important to consider your system’s particular specifications, the characteristics of the data you need to store, and how you might be using the data you have.
What are your system’s particular specifications?
First off, you need to look at your system and the technologies you use on a daily basis. Not all tools will support all of the data formats. Will you use specific tools to load, query, and analyze the data? It makes a difference.
How about your system’s storage and memory capabilities? Are there constraints in either of those areas? Some file formats are more compressible than others. If you have limited storage space on your system and anticipate huge data dumps, ORC and Parquet compress very nicely. The downside is these highly compressed formats are memory hogs and you may have to finagle some settings for greater memory allocation.
What are the characteristics of data will you be storing?
When looking at the type of data you’ll be storing, it’s important to look at the characteristics of your business’s data. Is most of your raw data in CSV or regular text formats? If so, are you thinking you might want to store them that way? Those file formats are definitely more readable for the average person, but they have implicit column values which present problems when being split. The unique characteristics of XML and JSON also present issues when file splitting. And unfortunately, if files aren’t easily splittable, queries are substantially slower.
The best files for splitability are the more advanced data formats such as ORC, Sequence, Avro, and Parquet optimized for Hadoop (an open source distribution system for managing data processing and storage). The greater the splitability, the greater the speed when querying data, and for even faster querying, advanced columnar file formats such as ORC and Parquet are preferred. In these formats, large numbers of columns are permissible yet you can query just a small number of them to meet your data analysis needs. In addition, These columnar file formats offer not only greater query speed, but data compression to save not only time querying, but valuable space for data.
If you’ll be storing data that changes often, requiring adding columns or deleting columns, you’ll need to choose a data format that will help you deal with those changes. Parquet and Avro allow for adding columns; in addition, Avro allows for deleting columns. If your data is subject to a lot of change, Avro is a great option.
How do you plan use the data?
The best format for your business type depends on your plans for using your data. Capitalizing on the strengths of the different formats, understanding the weaknesses of each one, and then measuring those characteristics against your data use needs will help you determine which data file format is best for you.
If space isn’t a problem and you don’t care about longer query times for larger datasets–if you just want to add data quickly into an easily readable format, text files, JSON, and CSV formats may be preferable for you and your business.
If space is at a premium and you have extremely large amounts of constantly evolving data that you need to be able to quickly query, Avro may best meet your needs. Columnar formats such as ORC or Parquet may be your formats of choice if your business’s primary need is data analysis at the fastest querying speeds.
So do you know which type of file format you’ll choose? Do you have sufficient storage space and memory? Is readability or query speed most important to you? Is your data complex and constantly revolving? Is your head spinning from indecision?
Are there easier ways to deal with data file formats?
If all of this still seems a bit overwhelming for you, there are options that allow you to transform and clean up your data into the most useable formats for your needs without necessarily optimizing to just one file format. These options allow you to choose file formats that work best for individual projects. Technological tools are then employed to extract and transform data to similar formats for analysis. The two main options are ETL (extract, transform, and load) tools and data wrangling tools.
You can plan to use traditional ETL processes to send well-structured data files down the pipeline to a outside, centralized data warehouse for reporting and analyzing. Or you can plan on employing data wrangling tools to allow you or your in house analysts, users, and managers–individuals who truly know and understand your particular business–to explore and prepare the data for analytical reports. If you need to be able to extract large-scale data or complex raw sources of data that comes in a variety of shapes and sizes, from a variety of sources, ETL may not be the best solution; data wrangling may be the best way to meet your needs if your data is highly varied and includes data from web sources.
So many choices. And that’s a good thing. Decide what your system can handle, the complexity and variety of data you have, and what you want to do with your data and go from there. If you want to simplify your data format management, look into the amazing technological tools available such as traditional ETL and data wrangling.
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