Upgraded to chromedriver 99.0.4844.51 on Raspbian(bullseye) and seeing this in your chromedriver.log? [0312/111354.689372:ERROR:egl_util.cc(74)] Failed to load GLES library: /usr/lib/chromium-browser/libGLESv2.so: /usr/lib/chromium-browser/libGLESv2.so: cannot open shared object file: No such file or directory [0312/111354.709636:ERROR:viz_main_impl.cc(188)] Exiting GPU process due to errors during initialization [0312/111354.735541:ERROR:gpu_init.cc(454)] Passthrough is not supported, GL is disabled, ANGLE is Add “–disable-gpu” as an option when setting up the browser. e.g. for selenium/java: ChromeOptions options = new ChromeOptions() options.addArguments("–disable-gpu") It looks like the behaviour has changed as this “shouldn’t” be required.

One hour ago: bq cp mydataset.table@-3600000 mydataset.table_restored Absolute (ms since UNIX epoch) GMT: Wednesday, 26 May 2021 13:41:53 = 1622036513000 https://www.epochconverter.com/ bq cp mydataset.table@1622036513000 mydataset.table_restored More on Bigquery time travel

Pseudo-Random Number Generators (PRNGs) are getting a major update in Java with the release of JEP 356. New interfaces and implementations make it easier to use different algorithms interchangeably and offer better support for stream-based programming. This is a great improvement for Java developers who require randomness in their applications. The JDK is constantly evolving and improving, and part of that process is ensuring that internal APIs are properly encapsulated. JEP 403 represents a step in that direction, by removing the –illegal-access flag.

To enable BigQuery row-level security, you can follow these steps: Go to the BigQuery console and select the Datasets tab. Click the name of the dataset that you want to enable row-level security for. Click the Security tab. Click the Row-level security tab. Click the Create policy button. In the Create policy dialog, enter a name for your policy. In the Principals section, select the users or groups that you want to grant access to the data.

Feature Confluent Cloud Kafka Google Cloud Pubsub Notes Data Retention Set retention per topic in Confluent Cloud, including unlimited retention with log compaction. Retains unacknowledged messages in persistent storage for 7 days from the moment of publication. There is no limit on the number of retained messages. Have to write custom subscriber/publisher to save beyond 7 days [L] + ongoing BAU [S] Replay A consumer request an “offset”, however the retention period is dictated by the broker config “Snapshots” can be created for later replay by these are limited to 7 days as per retention policy.

GCP Cloud IPs by region IP range with geolocation More info here

Apache Beam is an open-source unified programming model and framework for defining and executing big data processing pipelines. It provides a way to write data processing code that is portable across different execution engines or runtimes, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. Apache Beam’s portability framework allows you to write your data processing logic once and then run it on different execution engines without modifying the code.

Terraform’s Cloud Development Kit (CDK) let’s you use other languages to define your cloud infra. https://github.com/hashicorp/terraform-cdk/blob/master/examples/

No more copying and pasting modules.