PySpark Pandas All code can be downloaded below and you can run it complete for free in Google Colab. from pyspark.sql import functions print(dir(functions)) AutoBatchedSerializer Column DataFrame DataType PandasUDFType PickleSerializer PythonEvalType SparkContext StringType ascii asin atan atan2 basse64 bitwiseNOT blacklist UserDefinedFunction abs acos add_onths approxCountDistinct apprrox_count_distinct array array_containsss asc broadcast bround cbrt ceil coalesce col […]
Apache Spark
Speed
Run workloads 100x faster.
Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine.
Ease of Use
Write applications quickly in Java, Scala, Python, R, and SQL.
Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells.
Generality
Combine SQL, streaming, and complex analytics.
Runs Everywhere
Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.
Spark vs Pandas vs Dask
So if you know Pandas why should you learn Apache Spark? Pandas features: Tabular data ( and here more features than Spark ) Pandas can handle to million rows Limit to a single machine Pandas is not a distributed system. Dask vs Spark Apache Spark Dask Language Scala, Java, Python, R, SQL Python Scale 1-1000 […]
Databricks Setup
Apache Spark and Databricks are getting more and more popular. 2018 and 2019 ist was the most important language zu learn. In our next days we go throw the most important steps about Azure Databricks and Apache Spark. So first why is Apache Spark so popular? With Apache Spark you can run programs 100 times […]