Pinned Repositories
anagha-google's Repositories
anagha-google/s8s-spark-mlops-lab
anagha-google/cloud-composer-setup-foundations
anagha-google/table-format-lab-delta
anagha-google/ts22-just-enough-terraform-for-da
anagha-google/s8s-spark-ce-workshop
anagha-google/dataproc-labs
anagha-google/spark-on-gcp-gce
anagha-google/composer2-basic-orchestration
anagha-google/apache-hudi-gcp-lab
anagha-google/biglake-finegrained-lab
Demo for using fine grained permissions with BigLake and a Dataproc Personal Cluster
anagha-google/dataplex-on-gcp-lab-resources
anagha-google/spark-on-gcp-s8s
anagha-google/spark-on-gcp-with-confluent-kafka
anagha-google/dataplex-labs-ak
Dev version of GCP labs
anagha-google/azure-kusto-labs
Self-contained, hands-on-labs with detailed and step-by-step instructions, associated collateral (data, code etc) on trying out various features and integration points of Azure Data Explorer (Kusto)
anagha-google/bhoomi-spark
anagha-google/bq-log-analytics-k8s-demo
Sample cloud-native application with 10 microservices showcasing Kubernetes, Istio, gRPC and OpenCensus.
anagha-google/confluent-google-examples
Examples to what it pertains to Confluent and GCP architectures
anagha-google/data-beans-copy-20240327
anagha-google/data-engineering-spotlight-spark-gcp
anagha-google/dataplex-labs
anagha-google/dataplex-old
anagha-google/dataproc-templates
Google-provided Dataproc Serverless templates and pipelines for solving simple in-Cloud data tasks
anagha-google/ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
anagha-google/ray-labs
anagha-google/ray-summit-2023-training
anagha-google/serverless-spark-workshop
Solution Accelerators for Serverless Spark on GCP, the industry's first auto-scaling and serverless Spark as a service
anagha-google/spark-with-vertex-ai
anagha-google/table-format-lab-iceberg
anagha-google/techcon23-datalake-lab