What “SAP Databricks” means for notebooks
SAP Databricks is a Databricks edition integrated with SAP Business Data Cloud/Datasphere for governed SAP data products, sharing, and serverless access. Its user‑guide pages emphasize Python & SQL notebooks, built‑in visualizations, and serverless compute within the SAP context. SAP Databricks tenants, speak primarily about Python + SQL, Scala and R.
From the SAP Databricks docs: “Databricks notebooks in SAP Databricks support Python and SQL, and allow users to embed visualizations alongside links, images, and commentary written in markdown.”
Supported languages (and how they’re used)
1) Python
Best for: data wrangling with PySpark, ML (scikit‑learn, MLflow), plotting, notebooks with visualizations and debugging.Notebook specifics: SAP Databricks exposes an interactive Python debugger and notebook‑scoped package management (via %pip).General Databricks guidance: Python is a top recommendation for new projects.
# PySpark DataFrame example
df = spark.table(“sap_data.cashflow.cashflowforecast”) # Example mounted SAP catalog path
df.groupBy(“CompanyCode”).agg({“Amount”:”sum”}).show(20)
2) SQL
Best for: ad‑hoc analytics, BI‑style queries, quick aggregations, and powering built‑in notebook visualizations.Where it runs: against your lakehouse tables (Unity Catalog) and Serverless SQL warehouses for low‑friction, governed access.
— Query SAP data product mounted in Unity Catalog
SELECT CompanyCode, SUM(Amount) AS TotalAmount
FROM sap_data.cashflow.cashflowforecast
GROUP BY CompanyCode
ORDER BY TotalAmount DESC
“
3) Markdown (for narrative + visuals)
Purpose: structure your notebook/blog‑style narrative, add images/links, and keep analysis reproducible and readable.Status: available in notebooks for inline documentation in both SAP Databricks and the general platform.
%md
### Cash Flow by Company Code
This section analyzes forecasted cash flow from SAP data products mounted in Unity Catalog.
“
Package management in notebooks
Use %pip install <package> to install Python libraries at notebook scope, improving reproducibility and avoiding cluster‑wide changes. This is supported on Databricks runtimes and is commonly documented for notebook productivity.
%pip install prophet
from prophet import Prophet
Visualizations
Both SQL and Python cells can produce tabular outputs that you can convert into built‑in charts directly from the result UI—handy for executive readouts without exporting to separate BI tools.
Serverless compute notes (useful context for your readers)
SAP Databricks encourages serverless for both notebooks and SQL, which simplifies cluster management and speeds up ad‑hoc work. Great to highlight for analysts and data scientists who don’t want to babysit clusters.
What “SAP Databricks” means for notebooksSAP Databricks is a Databricks edition integrated with SAP Business Data Cloud/Datasphere for governed SAP data products, sharing, and serverless access. Its user‑guide pages emphasize Python & SQL notebooks, built‑in visualizations, and serverless compute within the SAP context. SAP Databricks tenants, speak primarily about Python + SQL, Scala and R.From the SAP Databricks docs: “Databricks notebooks in SAP Databricks support Python and SQL, and allow users to embed visualizations alongside links, images, and commentary written in markdown.”Supported languages (and how they’re used)1) PythonBest for: data wrangling with PySpark, ML (scikit‑learn, MLflow), plotting, notebooks with visualizations and debugging.Notebook specifics: SAP Databricks exposes an interactive Python debugger and notebook‑scoped package management (via %pip).General Databricks guidance: Python is a top recommendation for new projects.# PySpark DataFrame example
df = spark.table(“sap_data.cashflow.cashflowforecast”) # Example mounted SAP catalog path
df.groupBy(“CompanyCode”).agg({“Amount”:”sum”}).show(20)2) SQLBest for: ad‑hoc analytics, BI‑style queries, quick aggregations, and powering built‑in notebook visualizations.Where it runs: against your lakehouse tables (Unity Catalog) and Serverless SQL warehouses for low‑friction, governed access.– Query SAP data product mounted in Unity Catalog
SELECT CompanyCode, SUM(Amount) AS TotalAmount
FROM sap_data.cashflow.cashflowforecast
GROUP BY CompanyCode
ORDER BY TotalAmount DESC
“3) Markdown (for narrative + visuals)Purpose: structure your notebook/blog‑style narrative, add images/links, and keep analysis reproducible and readable.Status: available in notebooks for inline documentation in both SAP Databricks and the general platform.%md
### Cash Flow by Company Code
This section analyzes forecasted cash flow from SAP data products mounted in Unity Catalog.
“Package management in notebooksUse %pip install <package> to install Python libraries at notebook scope, improving reproducibility and avoiding cluster‑wide changes. This is supported on Databricks runtimes and is commonly documented for notebook productivity.%pip install prophet
from prophet import ProphetVisualizationsBoth SQL and Python cells can produce tabular outputs that you can convert into built‑in charts directly from the result UI—handy for executive readouts without exporting to separate BI tools.https://community.sap.com/t5/technology-blog-posts-by-sap/sap-databricks-plotly-a-deadly-match-for-analytics-excellence/ba-p/14247815 Serverless compute notes (useful context for your readers)SAP Databricks encourages serverless for both notebooks and SQL, which simplifies cluster management and speeds up ad‑hoc work. Great to highlight for analysts and data scientists who don’t want to babysit clusters. Read More Technology Blog Posts by SAP articles
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