Query Bytes’ are a unit of measure to track the compute power used when working with data.
At Panoply, they also happen to be the main differentiator between our different pricing tiers. In our usage-based model, Query Bytes are the amount of data processed when a query statement is run, manipulations are made to tables, or data is synced.
While we’d love to chat with you about how much data you’re likely to use each month, you can get a good estimate for yourself during our free 60-day Proof of Value.
While broadly speaking more data sources and more queries are going to equate to more Query Bytes, there are a few ways to optimize your data to keep your usage in check.
How to keep your query bytes under control
Regardless of how much data you’ll use, if cost—or outright efficiency—is a concern, there are definitely ways to control your query bytes usage.
- By minimizing the amount of data in a query result, the benefits can compound. If an analyst writes a query which only selects the appropriate columns, any analytics downstream (ML models applied, data visualization hosted in the cloud, etc.) will all require less storage and processing, cutting costs even further.
- Design queries that return concise results when querying large datasets.Writing queries which return only relevant attributes instead of entire tables will result in lower query byte usage. For example, using a SELECT * statement will return unrelated results and increase usage-related charges. By writing queries in such a way that only the relevant observations are returned—for example, by using a WHERE clause—you can reduce costs.
- Use filters wisely to limit the data processed. Ultimately, taking the time to filter out irrelevant data before Panoply processes a query can save you valuable bytes...and some money.
Data analysts are often tasked with providing valuable insights in short timelines. Small and mid-sized companies may be dependent on brittle spreadsheets and labor-intensive, manually generated reports. Panoply offers a simplified workflow capable of improving analytical capabilities, speed, and scalability for data practitioners and stakeholders alike.
By offloading data engineering responsibilities, analysts are able to focus on their strengths: identifying appropriate data for problem-solving and providing critical insights to their organizations.
Though Panoply provides a streamlined environment for data connections, storage, and scalable analytics, keeping costs down is always an important consideration. By writing efficient SQL scripts, data analysts can cut costs while enjoying the advantages of Panoply.
Disclaimer: At Panoply we, like most of the IT and data storage industry, measure bytes using gibibytes based on the binary system. For more information on gibibyte measurement, click here.
If you’d like more info on what Panoply does and whether it might be right for your business, book a personalized demo today.