DZone
AUGUST 21, 2021
This article describes 3 different tricks that I used in dealing with big data sets (order of 10 million records) and that proved to enhance performance dramatically. Trick 1: CLOB Instead of Result Set.
Dynatrace
FEBRUARY 16, 2023
As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022. And without the encumbrances of traditional databases, Grail performs fast. “In
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Dynatrace
APRIL 20, 2023
Using Grail to heal observability pains Grail logs not only store big data, but also map out dependencies to enable fast analytics and data reasoning. ” Weighing the value and cost of indexed databases vs. Grail With standard index databases, teams must choose relevant indexes before data ingestion.
Dynatrace
MARCH 11, 2021
I took a big-data-analysis approach, which started with another problem visualization. I wanted to understand how I could tune Dynatrace’s problem detection, but to do that I needed to understand the situation first. To achieve that I took two approaches: Visualizing historic problem data via a “Swimlane Visualization”.
Dynatrace
SEPTEMBER 15, 2020
Stefano started his presentation by showing how much cost and performance optimization is possible when knowing how to properly configure your application runtimes, databases, or cloud environments: Correct configuration of JVM parameters can save up to 75% resource utilization while delivering same or better performance!
Percona
JANUARY 19, 2023
Some startups adopted MySQL in its early days such as Facebook, Uber, Pinterest, and many more, which are now big and successful companies that prove that MySQL can run on large databases and on heavily used sites. It was developed for optimizing data storage and access for big data sets.
Percona
JANUARY 14, 2019
What if we use ClickHouse (which is a columnar analytical database) as our main datastore? Well, typically, an analytical database is not a replacement for a transactional or key/value datastore. Although such databases can be very efficient with counts and averages, some queries will be slow or simply non existent. Processed 4.15
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