What are Panoply Query Bytes?

We recently updated our pricing model to better match our users’ expectations. Now, access to all our native data sources is included—that’s right, all the connectors you want, no extra fees!—as is access to our incredible Support team. 

Our new usage-based pricing model is based on 3 related elements:

1. Rows extracted

The total number of rows successfully extracted from a data source. We don't count subtables and nested data against your total, so you pay for the rows you bring in and nothing else.

2. Storage

The amount of data you store in Panoply. (Simple, right?)

3. Query bytes

The amount of data—rounded up to the nearest 10MB—processed when a query statement is run, manipulations are made to tables (i.e. adding observations, adding additional attributes, joining tables, etc.), or data is synced. 

Want more detail? Get the complete picture on our pricing page → 

While we’d love to chat with you about how much data you’re likely to use each month, you can also feel out your storage needs during a free trial.

How to keep your query bytes under control

Regardless of how much data you’ll use, if cost—or outright efficiency—is a concern (and when isn’t it?), 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.

Our best advice to keep your query bytes under control is to 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.

Ultimately, taking the time to filter out irrelevant data before Panoply processes a query can save you valuable bytes...and some money.

Conclusion

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.

Panoply is an ETL and data warehousing solution that supports high-speed SQL queries and utilizes the processing power of Google’s robust cloud infrastructure. Panoply’s separation of storage and compute means users pay only for what they use while still getting the convenience of automatically allocated analytical and storage resources as their data needs change. 

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.

If you’d like more info on what Panoply does and whether it might be right for your business, book a personalized demo today.

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