Mastering Data Wrangling with PySpark in Databricks

Mastering data wrangling with PySpark in Databricks teaches how PySpark uses in-memory cluster computing, data frames, and RDDs to analyze large datasets, with Databricks as a collaborative, notebook-based platform.

Set up your Databricks environment by creating a community edition account, launching a cluster, and starting a notebook to connect to the cluster and run Python, SQL, Scala, or R.

Take an inside look at Databricks, including notebooks, clusters, data, and the spark UI. Learn to attach notebooks to a cluster, run code, and switch languages in notebooks.

Learn how spark uses lazy evaluation to optimize workflows by separating transformations, which create data frames, from actions, which return calculated results; explore examples like filter, group by, and aggregates.

Explore the basic data structures in PySpark, including lists, tuples, dictionaries, and RDDs versus data frames. Learn to parallelize lists into RDDs and create PySpark data frames from rows.

Define a schema and data types in PySpark, using struct field and struct type to map variables to double, integer, and string, then apply it to correct depth and price.

Create dataframes in Databricks with PySpark by defining a schema with StructType and StructField and the date, product, store, and total fields, then spark create dataframe from data and schema.

Explore multiple PySpark techniques to create data frames in Databricks, including using a struct schema, parallelizing lists into an RDD, building with createDataFrame from tuples, and Pandas on Spark.

Master importing PySpark functions in Databricks, with from pyspark.sql.functions and optional aliasing, using call, count distinct, sum, min, max, and mean; leverage window techniques over loops for big data.

Learn to load the diamonds csv dataset in Databricks with PySpark, set header to true, cast columns to float, select and display data, and visualize results.

Infer schema automatically infers data types when loading csv datasets in Databricks, avoiding manual struct definitions and correcting types for price, x, y, z.

Develop data wrangling skills in pyspark by selecting data, slicing and filtering, handling missing values, joining datasets, and using groupby and pivot tables for summarizing.

Learn to select, add, and remove columns in the diamonds dataset with PySpark on Databricks, convert key fields to float, and rename or alias columns using withColumn and select.

Learn how to rename columns in a Databricks PySpark DataFrame using withColumnRenamed or select with alias. See how these methods rename specific columns versus all columns.

Master data wrangling with PySpark in Databricks by counting rows, estimating counts with samples, identifying distinct values, sorting by columns, and casting data types.

Apply filter and where to narrow datasets in PySpark on Databricks, selecting premium cuts and price thresholds to accelerate analysis and enable focused visualizations.

Apply contains and like text filters in PySpark on Databricks to select rows by substring, starts with, ends with, or middle patterns using the dataset filter function.

Demonstrate using is in and between to filter data in PySpark on Databricks, showing value lists, date ranges, and using where as an alternative to filter.

Master filling and replacing missing values in PySpark on Databricks, using is null and is nan, casting to integer, and DataFrame operations like replace, lit, and fill to clean data.

Explore missing data handling in Databricks PySpark: drop with any, all, and thresh to keep rows with non-null values, and fill with mean or a string placeholder.

Leverage the case when function in PySpark on Databricks to replace many if statements, labeling data as expensive, regular, or cheap using when and is in.

Group by cut and aggregate with PySpark in Databricks on diamonds dataset to compute counts, mean (average) price, min, max, percentiles, stats, then alias columns and sort by average price.

Master data wrangling with PySpark pivot tables by transforming long data to wide, grouping by cut and color, and using pivot to create color columns with min price insights.

Explore window functions in PySpark, derived from SQL, to compute ranks, rolling sums, and cumulative distributions, using window specs with partition by and order by on diamonds dataset.

Master joining datasets in PySpark on Databricks with left, right, inner, full outer, and left anti join to combine stores and sales, then analyze total sales by city.

Learn how to use the percentile function in PySpark to compute medians and percentiles for price data, and build a distribution using 25th, 50th, 75th, and 95th percentiles.

PySpark SQL adds median in 3.5.0. The lecture compares median and percentile(50) on the diamonds dataset grouped by cut, showing equivalent results.

Explore PySpark SQL functions such as sample with seed, data dimensions, list values per row, greatest across columns, floor and ceiling, group by with collect list, and file size checks.

Demonstrate data caching with PySpark in Databricks by caching a 60 million row grouped dataset to memory, revealing speedups for repeated filters, while noting caching may underperform on small datasets.

Save files to the Databricks file system using parquet for better data type preservation and compression, then load with Spark to verify the saved data.

Practice data wrangling with PySpark in Databricks through hands-on exercises. Load, filter to ideal diamonds, create a price_tier, map tiers to numbers, perform a left join, and save to DBFS.

Load a diamond dataset with spark.read.csv, filter to ideal cuts, create a price tier with when-otherwise, left join datasets, aggregate counts and mean price, and save as a Databricks table.

Optimize PySpark queries in Databricks by using dataframes, filtering early, and avoiding user-defined functions. Cast big integers to text for exact filters and leverage partitioning to speed joins.

Learn how cache and persist store Spark data on worker nodes to speed up computations, with lazy evaluation and actions triggering runs, choosing memory or disk.

Explore handling large datasets in Databricks with PySpark by caching data, and applying bronze–silver–gold medallion architecture to clean, transform, and deliver ready-to-use data.

Explore the data frame API vs SQL API in Databricks, highlighting when to use PySpark for complex transformations and machine learning, versus SQL for simple data manipulation and readability.

Explore working with SQL in Databricks by loading the diamonds data, registering a temp view, and querying with SQL to retrieve data and compute average price by cut.

Learn basic sql queries in Databricks on the diamonds dataset, grouping by cut and computing min, max, and percentile prices to explain how distribution affects averages.

Explore modeling with PySpark and MLlib, contrasting spark ml and scikit-learn workflows, including vectorization, feature engineering, and pipelines for classification and regression.

Analyze the diamonds history with PySpark for exploratory data analysis, visualize distributions, and build a linear regression model to price diamonds using cut, color, and the x, y, z dimensions.

Explore diamond prices with PySpark in Databricks, grouping by cut and color to compute total, mean, median, and price per carat. Reveal strong price drivers through carat correlations (about 0.92).

Explore how to build a PySpark MLlib regression model in Databricks, using vector assembler and log transformations to predict diamond prices from carat and assess residuals.

Build a logistic regression model in PySpark MLlib using a pipeline with VectorAssembler, StringIndexer, and OneHotEncoder to prepare the UCI adult census data, handling missing values, for income prediction.

Apply feature engineering with a univariate selector to choose categorical and numerical variables, transform data into a vectorized frame, and output features and the income label.

Explain handling the unbalanced output variable by transforming categorical features to numeric using string indexer and one-hot encoding, assembling vectorized features in a modeling pipeline.

Prepare features with string indexing and one-hot encoding, split data for training and testing, fit a logistic regression model, and evaluate precision, recall, and specificity from the confusion matrix.

Explore logistic regression in PySpark, tune thresholds and regularization, and use cross-validated parameter grids to balance precision and recall for imbalanced binary classification.