2023 promises to be a growth year in the adoption of #Apache#Iceberg as a format for both data lakes and warehouses. This post explains why: https://t.co/mQzZu2YajB #googlecloud#bigquery
About to kick off the @rapha#festive500 2022! The goal is to ride 500k’s between now and New Years.
Details: https://t.co/Uc0XZt1Iod
Good luck to everyone participating!
The BigQuery Source for Datastream is now in preview. This provides an all batteries included approach to replicating data from MySQL, Oracle, or PostgreSQL to BigQuery without having to spin up any additional services. #bigquery#GoogleCloud
https://t.co/rYvsVRwr5c
And my favorite product announcement since joining Google Cloud three years ago is:
BigQuery Big Lake Tables - https://t.co/6NjJqNGzpH
Hope you enjoyed my top ten favorite product announcements since joining @googlecloud three years ago.
🧵4/4
Can’t believe it has been 3 years— the time has gone so fast! So much has changed since then and there have been so many amazing product announcements. Let me take a moment to list my top 10 favorites. 🧵
Great write-up on how to move data from #Salesforce into #BigQuery. There are a lot of solutions out there-- based on my experience this is one of the best ones I have seen. https://t.co/5bTiVEagxL #dataengineering#apachekafka
You can now write PySpark code in the BigQuery web console and have it run using Serverless Spark!
Separation of compute and storage continues -- this is Hadoop compute + BigQuery storage. Through a single pane of glass that supports both SQL and Spark.
https://t.co/UGKB0S99Bh
#Dataproc#Serverless is now GA! “Dataproc Serverless lets you run Spark batch workloads without requiring you to provision and manage your own cluster.” — https://t.co/isJbobk7Zp @googlecloud#Spark
Really cool new feature— #BigQuery standard SQL now supports the JSON data type for storing JSON data (in preview). This really opens up a lot of new use cases especially around APM and Logging. https://t.co/JjQSkgC5zT #GoogleCloud
Spent a bit of time over the weekend comparing the #Dataframe Performance of #Pandas on #Spark 3.2.0 vs #Pandas -- here are my thoughts summarized in a blog post: https://t.co/lEgP24JQ06 #bigdata#python