The Netflix TechBlog
OCTOBER 18, 2022
We want users to rely on shared templates and reuse their workflow definitions across their team, saving time and effort on creating the same functionality. Maestro high level architecture In Maestro, a workflow is a DAG (Directed acyclic graph) of individual units of job definition called Steps.
The Netflix TechBlog
JUNE 14, 2023
A single API team maintained both the Java implementation of the Falcor framework and the API Server. And we definitely couldn’t replay test non-functional requirements like caching and logging user interaction. Phase 1 Created a GraphQL Shim Service on top of our existing Monolith Falcor API.
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The Netflix TechBlog
APRIL 12, 2022
How do our users tell us which portion and, even more critically, given that the portion of the graph of interest will almost definitely span data exposed by many services, how do we keep the index current with all these various services? This was done using graphql-java.
The Netflix TechBlog
JULY 26, 2021
CDC events can also be sent to Data Mesh via a Java Client Producer Library. For the past few years, many of these trackers were powered by hand-curated SQL scripts and API calls being managed by CRON schedulers implemented in a Java Service called Lego. Please stay tuned! Endnotes ¹ Inmon, Bill. Dehghani, Zhamak.
Dynatrace
FEBRUARY 18, 2021
Definitely not. NET or Java footprint (as diagnostic tools are especially useful for these). Stay tuned for even more improvements to diagnostic workflows in Dynatrace. Does this mean that we shouldn’t try different terminology and navigation patterns and should just repeat whatever is industry standard? What’s next?
Percona
MARCH 23, 2023
It has connectors for programming languages such as Java, Python, and PHP, as well as integrations with popular data visualization tools such as Tableau and Power BI. If you are looking for a database system for your enterprise-level application, PostgreSQL is definitely worth considering.
The Netflix TechBlog
AUGUST 22, 2019
The configuration file, referenced scripts, and artifact dependency definitions all live in an internal git repository. Running a canary for each change and testing the AMI in production allows us to capture insights around impact on Windows updates, script changes, tuning web server configuration, among others.
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