Results for “pyspark”

Design, build, and optimize Microsoft Fabric Lakehouse solutions with guidance on core concepts, components, security, shortcuts, and performance optimization.

Generate Python SDK setup, CRUD, bulk, and paging snippets for Microsoft Dataverse using official patterns.

Scales pandas and NumPy workflows to datasets larger than memory using parallel and distributed computing, with support for dataframes, arrays, bags, and custom task graphs.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.

Build, fit, validate, and compare Bayesian models using PyMC's modern API, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

SQLAlchemy Python SQL toolkit and ORM with powerful query builder, relationship mapping, and database migrations via Alembic

Control liquid handling robots, plate readers, pumps, and other lab equipment through a unified Python interface across platforms.

Read, write, and manipulate genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

Process large-scale ML datasets with distributed streaming execution across CPU/GPU, supporting Parquet, CSV, JSON, images, and integration with PyTorch, TensorFlow, and Ray Train.

Expert guidance for Ibis, the Python dataframe library that provides a pandas-like API but generates SQL for execution on any backend — DuckDB, PostgreSQL, BigQuery, Snowflake, Spark, and more. Helps developers write analytics code once and run it anywhere without rewriting SQL for each database.

Dask parallel computing reference for Python. Covers Dask DataFrame (parallel Pandas), Dask Array (parallel NumPy), Dask Delayed for custom parallelism, Dask Bag, distributed clusters, dashboard monitoring, and scaling best practices.

Write, optimize, and debug Apache Spark jobs for high-performance distributed data processing, ETL pipelines, and big data workloads.