Better than traditional pivot tables, the Group feature in Gigasheet allows you to quickly pivot and segment your data. Best of all it works on huge CSVs, flattened JSON files, EVTX files and more. Try this example 👉
Grouping organizes rows by a specified categorical column across your entire data set. To the right of each group you'll see a count of the number of rows within the group. You even can create nested groups, and rearrange the group hierarchy via the bar at the top of the sheet, or in the Group panel.
Calculate sum, average, percentages, count unique values, find missing data and more – all without writing a formula. Aggregations are great for relationship analysis in netflow, or other logs. Try this example 👉
Use aggregations instead of formulas for big CSVs
Gigasheet Aggregations make it easy to calculate summary statistics for a given column. This is similar to a formula in a spreadsheet, but doesn't require you to learn any special syntax. The example above shows how to count unique values in a column, and how to calculate a sum and an average. Aggregations work on filtered values only. If a filter removes rows from a group, the aggregation for the group only considers rows remaining after the filter is applied. An especially handy calculation for date-time data is the Range aggregation, which returns the elapsed days or months in a given data set. Check out the docs on all of the Aggregations supported in Gigasheet.
Add geographic location to your bulk IP data. Gigasheet makes it easy to perform tons of GEOIP lookups without writing any code. Try this example 👉
Gigasheet's GEO IP Enrichment adds columns to your sheet with a given IP's geolocation data. To use the GEO IP lookup feature:
1. Click the column menu for an IP address column and select Enrich.
2. In the Enrichments popup check GEO IP and click Enrich Data.3. The geolocation data will appear in columns immediately to the right of the selected IP column.
Result include the following fields (when available): Country, Country Code, City, County, Region, Latitude, Longitude, and GEOHASH.
Here's how Gianna Perez describes it:
"Humans, in most cases, are not built to process and conceptualize data in any significant measure or speed.
Notwithstanding, the last several years have seen an unprecedented growth in data collection and ingestion techniques driven by newer forms of network and cloud technologies, arousing a particular (and ever-growing) concern among the cybersecurity community as diminished visibility threatens to grow proportionally to the degree of integration.
In other words, organizations should be asking themselves if the logs and data they’re collecting are actually telling the whole story and, if they are, is the human component, namely the incident responders and threat hunters at the crossroads, able to quickly align itself with what really took place.
There is, however, a new tool on the horizon that threatens to disrupt the old paradigm of looking endlessly at relational entities, such as spreadsheets, in search of the mythical “Aha!” moment: Gigasheet.
Combining the succinct dimensionality of structured data with a powerful analytics engine capable of handling billions of data points at a time, Gigasheet will certainly innovate the prescriptive space where data can be manipulated, aggregated, queried, and analyzed under a single web-based ecosystem that is as broadly intuitive as it is powerful."