Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
Here is a tool availableelsevierlabs-os/NotebookDiscovery: Notebook Discovery Tool for Databricks notebooks (github.com)How to Catalog and Discover Your Databricks Notebooks Faster - The Databricks Blog
Accessing the regions that are disabled by default in AWS from Databricks.In AWS we have 4 regions that are disabled by default. You must first enable it before you can create and manage resources. The following Regions are disabled by default:Africa...
Hi all,I'm working with event hubs and data bricks to process and enrich data in real-time.Doing a "simple" test, I'm getting some weird values (input rate vs processing rate) and I think I'm losing data:If you can see, there is a peak with 5k record...
hi @Jhonatan Reyes ,How many Event hubs partitions are you readying from? your micro-batch takes a few milliseconds to complete, which I think is good time, but I would like to undertand better what are you trying to improve here.Also, in this case ...
If I run some code, say for an ETL process to migrate data from bronze to silver storage, when a cell executes it reports num_affected_rows in a table format. I want to capture that and log it in my logger. Is it stored in a variable or syslogged som...
Hi @xiaojun wang please check the blog and let us know if this helps you.https://databricks.com/blog/2015/07/15/introducing-window-functions-in-spark-sql.html
Scenario : I have a dataframe that have 5 billion records/rows and 100+ columns. Is there a way to write this in a delta format efficiently. I have tried to export it but cancelled it after 2 hours (write didnt finish) as this processing time is not ...
Hi @Franco Sia ,I will recommend to avoid to use the repartition(50), instead enable optimizes writes on your Delta table. You can find more details hereEnable optimized writes and auto compaction on your Delta table. Use AQE (docs here) to have eno...
I have databricks runtime for a job set to latest 10.0 Beta (includes Apache Spark 3.2.0, Scala 2.12) .In the notebook when I check for the spark version, I see version 3.1.0 instead of version 3.2.0I need the Spark version 3.2 to process workloads a...
On Databricks, we use the following code to flatten JSON in Python. The data is from a REST API:```df = spark.read.format("json").option("header", "true").option("multiline", "true").load(SourceFileFolder + sourcetable + "*.json")df2 = df.select(psf....
@Dennis D , what's happening here is that more than 2 GB (2147483648 bytes) is being loaded into a single column value. This is a hard-limit for serialization. This KB article addresses it. The solution would be to find some way to have this loaded ...
Feature request: It is possible to add comments to both databricks sql databases and tables. It would be really usefull if these comments could show up (if they are provided) in PowerBI when one connects to the Databricks SQL endpoint, e.g. in this w...
I have a dataframe with the following columns:Key1Key2Y_N_ColCol1Col2For the key tuple (Key1, Key2), I have rows with Y_N_Col = "Y" and Y_N_Col = "N".I need a new dataframe with all rows with Y_N_Col = "Y" (regardless of the key tuple), plus all Y_N_...
I'd use a left-anti join.So create a df with all the Y, then create a df with all the N and do a left_anti join (on key1 and key2) on the df with the Y.then a union of those two.
What is the future of aws?The future of AWS is very promising. So, if you are thinking of a cloud career or want to switch your position to something related to the cloud, I would highly recommend you going for AWS training. No matter what field you ...
Basically I'm worried about the scenario where data that gets cached on Databricks SQL endpoints becomes out of sync with the source Delta table. If that were to happen and data was read from the cache it would be out of date/incorrect. Is this a con...
There are 3 types of caching. 1-Databricks SQL UI caching, 2-Query results caching , 3-Delta caching . (1) does not get invalidated. It's like your BI dashboard. BI dashboard needs to be manually refreshed.(2) and (3) gets auto invalidation.pls check...
In Spark SQL, you could use commands like "insert overwrite directory" that indirectly creates a temporary file with the datahttps://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-dml-insert-overwrite-directory.html#example...
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