I've recently started using the Databricks Standard SharePoint connector within my workspace and have run into some significant performance issues.My notebook does a straightforward read using the following:lakeflow_connection_name = 'sharepoint_dev'...
hello everyone, I'm trying use spotify's api to analyse my music data, but i'm receiving a error during authentication, specifically when I try get the token, above my code.Is it a databricks bug?pip install spotipyfrom spotipy.oauth2 import SpotifyO...
To get a Spotify API token, create an app in the Spotify Developer Dashboard and get your Client ID and Client Secret. Send a POST request to Spotify Accounts API using the Client Credentials Flow to receive an access token.For local services in Jed...
Need some advice from the community.I am processing around 100 million records using:df.select(required_cols).distinct().write.saveAsTable(...)The source has 1000+ columns, but I'm selecting only 20 columns before applying DISTINCT.I have already ena...
For 100M+ records, `DISTINCT` will almost always be shuffle-heavy because Spark must compare records across partitions. If you truly need exact deduplication on 20 columns, consider using drop Duplicates(required cols instead of distinct the executio...
After working on multiple modernization initiatives, I’ve noticed a pattern:Organizations spend months discussing:Databricks vs SnowflakeSpark vs SQLBatch vs StreamingAirflow vs Managed OrchestrationBut the biggest challenge is usually somewhere els...
I completely agree that teams often underestimate the metadata challenge during modernization.One thing I’ve seen repeatedly, though, is that the hardest part isn’t always the metadata itself—it’s the business intent behind it. We can extract mapping...
Our power BI reports consume real-time data , and for that the only option remains is Databricks serverless,but serverrless is expensive option, how to control the costs for serverless , and any other alternatives. Thank you.
Serverless is often the preferred option for Power BI DirectQuery workloads because it starts in seconds and scales automatically. However, it’s not always the only option, and there are several ways to reduce costs.A few high-impact optimizations:Se...
Hi,I have created 2 identical tables but one is partitioned and the one is a Liquid Clustered with Auto Clustering.I inserted 30M rows x 2 (60M) for two dates , date 1 = 2026-06-01 and date = 2026-06-02 , then I overwrite the date 2026-06-02 with a s...
Hi, the current way is not optimal. You can follow belowINSERT query ran with mostly 43 tasks, creating 43 output files. Since the Liquid clustered table has no organization (clusterBy "[]") - dates are randomly scattered across files.Partition table...
I'm prototyping a cluster cost / right-sizing advisor and wanted to get a reality-check from people running Databricks at real scale before I sink more time into it.The main thing I'm chasing is Photon fallback. Photon quietly drops to the JVM on uns...
Hello Community,We have been trying to migrate our jobs from Classic Compute to Serverless Compute. As part of this process, we face several challenges, and this is one of them.When we read CSV or JSON files with multiLine=true, the load becomes sing...
spark_partition_id is the closest and most performant function available as an alternative, and I migrated to use this function. So far, no issues.https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.spark_p...
Hello Community,We have been trying to migrate our jobs from Classic Compute to Serverless Compute. As part of this process, we face several challenges, and this is one of them.We have several scenarios where we need to send an inline email via Pytho...
The solution we implemented as an alternative for email sending from Serverless is via the Microsoft Graph API.https://learn.microsoft.com/en-us/graph/api/user-sendmail?view=graph-rest-1.0&tabs=python
HiWe are trying to generate fake data to run our tests. For example, we have a pipeline that creates a gold layer fact table form 6 underlying source tables in our silver layer. We want to generate the data in a way that recognises the relationships ...
Hi @Nick_Hughes ,I am very late to the party, but I was digging in the internet to find more people discussing a relatable problem for which I am on my way building a definitive solution, and came across your post from 3 years ago. Times have changed...
Hi,I need to compare the sizes of my delta tables , what's the correct approach ?Table size reported by analyze command ? , but how do I check the delta log size , if I enable CDF .. how do I know the CDF log size(the overhead it adds) ? , kind of l...
Hi @RGSLCA DESCRIBE DETAIL is the best starting point if you're comparing Delta table sizes, but it's important to understand what it reports. The sizeInBytes value represents only the latest active snapshot of the table, not the total storage consum...
As I understand it, streaming tables and materialized views produced by Lakeflow Spark Declarative Pipelines (DLT) are always Unity Catalog managed tables , there's no LOCATION/path option on create_streaming_table or apply_changes.Is that correct? A...
Hi @nidhin, What you’re saying is basically correct for a Unity Catalog-enabled Lakeflow Spark Declarative Pipelines setup. In that model, pipelines publish streaming tables and materialized views into the target catalog and schema, the data is store...
One pattern I keep seeing in data engineering projects:STTM is treated as documentation.But in reality, STTM can become much more than that.A well-structured Source-to-Target Mapping can act as a metadata contract between business, engineering, QA, a...
Hi,It is currently not possible to specify a list of tables to refresh and their refresh policies (full/normal) in a Lakeflow Job.It can be done via the REST API, but it's messy.For example, if you need some tables or views refreshed more regularly, ...
I’ve been exploring a metadata-driven approach to data engineering through a project called Data Engineering Copilot.The idea is to treat Source-to-Target Mapping (STTM) documents as structured metadata rather than static documentation.Instead of man...
This is a good discussion topic, but from my experience right now it is both meta data driven and most traditional excel based STMs.A few observations:How most teams manage STTM todayLevel 1 (Most Common)STTM in Excel, Word, or Confluence.Engineers m...
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