By: Girish Gowda
PySpark Fundamentals
1. Basic DataFrame Operations
1.
● Creating a DataFrame:
spark.createDataFrame(data)
2.
● Reading a CSV File:
spark.read.csv("path/to/file.csv")
3.
● Reading a JSON File:
spark.read.json("path/to/file.json")
4.
● Reading a Parquet File:
spark.read.parquet("path/to/file.parquet")
5.
● Writing DataFrame to CSV:
df.write.csv("path/to/output")
6.
● Writing DataFrame to JSON:
df.write.json("path/to/output")
7.
● Writing DataFrame to Parquet:
df.write.parquet("path/to/output")
8.
● Show DataFrame: df.show()
9.
● Print Schema: df.printSchema()
10. ●
Column Selection: df.select("column1", "column2")
11. ●
Filtering Rows: df.filter(df["column"] > value)
12. ●
Adding a New Column: df.withColumn("newColumn", expression)
13. ●
Renaming a Column: df.withColumnRenamed("oldName",
"newName")
14. ●
Dropping a Column: df.drop("column")
15. ●
Describe Data: df.describe().show()
16. ●
Counting Rows: df.count()
17. ●
Aggregating Data: df.groupBy("column").agg({"column":
"sum"})
18. ●
Sorting Data: df.sort("column")
19. ●
Distinct Values: df.select("column").distinct()
● Sample Data: df.sample(withReplacement, fraction, seed)
2. Advanced Data Manipulation
● Joining DataFrames: df1.join(df2, "column")
● Union DataFrames: df1.union(df2)
● Pivot Table:
df.groupBy("column").pivot("pivot_column").agg(count("another_column"))
● Window Functions:
from pyspark.sql import Window;
windowSpec =
Window.partitionBy("column")
● DataFrame to RDD Conversion: df.rdd
● RDD to DataFrame Conversion: rdd.toDF()
● Caching a DataFrame: df.cache()
● Uncaching a DataFrame: df.unpersist()
● Collecting Data to Driver: df.collect()
● Broadcast Join:
from pyspark.sql.functions import broadcast;
df1.join(broadcast(df2), "column")
3. Handling Missing and Duplicate Data
● Dropping Rows with Null Values: df.na.drop()
● Filling Null Values: df.na.fill(value)
● Dropping Duplicate Rows: df.dropDuplicates()
● Replacing Values: df.na.replace(["old_value"],
["new_value"])
4. Data Transformation
● UDF (User Defined Function):
from pyspark.sql.functions import udf;
udf_function =
udf(lambda z: custom_function(z))
● String Operations:
from pyspark.sql.functions import lower, upper;
df.select(upper(df["column"]))
● Date and Time Functions:
from pyspark.sql.functions import current_date,
current_timestamp;
df.select(current_date())
● Numeric Functions:
from pyspark.sql.functions import abs, sqrt;
df.select(abs(df["column"]))
● Conditional Expressions:
from pyspark.sql.functions import when;
df.select(when(df["column"] > value,
"true").otherwise("false"))
● Type Casting: df.withColumn("column",
df["column"].cast("new_type"))
● Explode Function (Array to Rows):
from pyspark.sql.functions import explode;
df.withColumn("exploded_column",
explode(df["array_column"]))
● Pandas UDF:
from pyspark.sql.functions import pandas_udf;
@pandas_udf("return_type")
def pandas_function(col1, col2): return operation
● Aggregating with Custom Functions:
df.groupBy("column").agg(custom_agg_function(df["another_column"]))
● Window Functions (Rank, Lead, Lag):
from pyspark.sql.functions import rank, lead, lag;
windowSpec = Window.orderBy("column");
df.withColumn("rank", rank().over(windowSpec));
● Handling JSON Columns:
from pyspark.sql.functions import from_json, schema_of_json;
df.withColumn("parsed_json",
from_json(df["json_column"], schema_of_json))
5. Data Profiling
● Column Value Counts:
df.groupBy("column").count()
● Summary Statistics for Numeric Columns: df.describe()
Correlation Between Columns:
df.stat.corr("column1", "column2")
● Crosstabulation and Contingency Tables:
df.stat.crosstab("column1","column2")
● Frequent Items in Columns:
df.stat.freqItems(["column1", "column2"])
● Approximate Quantile Calculation:
df.approxQuantile("column", [0.25, 0.5,0.75],
relativeError)
6. Data Visualization (Integration with other libraries)
● Convert to Pandas for Visualization:
df.toPandas().plot(kind='bar')
● Histograms using Matplotlib:
df.toPandas()["column"].hist()
● Box Plots using Seaborn: import seaborn as sns;
sns.boxplot(x=df.toPandas()["column"])
● Scatter Plots using Matplotlib:
df.toPandas().plot.scatter(x='col1', y='col2')
7. Data Import/Export
● Reading Data from JDBC Sources:
spark.read.format("jdbc").options(url="jdbc_url",
dbtable="table_name").load()
● Writing Data to JDBC Sources:
df.write.format("jdbc").options(url="jdbc_url",
dbtable="table_name").save()
● Reading Data from HDFS:
spark.read.text("hdfs://path/to/file")
● Writing Data to HDFS:
df.write.save("hdfs://path/to/output")
● Creating DataFrames from Hive Tables: spark.table("hive_table_name")
8. Working with Large Data
● Partitioning Data: df.repartition(numPartitions)
● Coalesce Partitions: df.coalesce(numPartitions)
● Reading Data in Chunks:
spark.read.option("maxFilesPerTrigger",1).csv("path/to/file.csv")
● Optimizing Data for Skewed Joins:
df.repartition("skewed_column")
● Handling Data Skew in Joins:
df1.join(df2.hint("broadcast"), "column")
9. Spark SQL
● Running SQL Queries on Data Frames:
df.createOrReplaceTempView("table");
spark.sql("SELECT * FROM table")
● Registering UDF for SQL Queries:
spark.udf.register("udf_name", lambda x:
custom_function(x))
● Using SQL Functions in DataFrames:
from pyspark.sql.functions import expr;
df.withColumn("new_column",
expr("SQL_expression"))
10. Machine Learning and Advanced Analytics
● VectorAssembler for Feature Vectors:
from pyspark.ml.feature import VectorAssembler;
assembler = VectorAssembler(inputCols=["col1",
"col2"], outputCol="features")
● StandardScaler for Feature Scaling:
from pyspark.ml.feature import StandardScaler;
scaler = StandardScaler(inputCol="features",
outputCol="scaledFeatures")
● Building a Machine Learning Pipeline:
from pyspark.ml import Pipeline; pipeline =
Pipeline(stages=[assembler, scaler, ml_model])
● Train-Test Split: train, test = df.randomSplit([0.7, 0.3])
● Model Fitting and Predictions:
model = pipeline.fit(train);
predictions = model.transform(test)
● Cross-Validation for Model Tuning:
from pyspark.ml.tuning import CrossValidator;
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid)
● Hyperparameter Tuning:
from pyspark.ml.tuning import ParamGridBuilder; paramGrid =
ParamGridBuilder().addGrid(model.param, [value1,
value2]).build()
11. Graph and Network Analysis
● Creating a GraphFrame:
from graphframes import GraphFrame;
g =
GraphFrame(vertices_df, edges_df)
● Running Graph Algorithms: results =
g.pageRank(resetProbability=0.15, maxIter=10)
● Subgraphs and Motif Finding: g.find("(a)-[e]->(b);
(b)-[e2]->(a)")
12. Streaming Data Processing
● Reading from a Stream:
spark.readStream.format("source").load()
● Writing to a Stream: query
=df.writeStream.outputMode("append").format("console").start()
● By: Girish Gowda
PySpark Fundamentals
1. Basic DataFrame Operations
1. ● Creating a DataFrame: spark.createDataFrame(data)
2. ● Reading a CSV File: spark.read.csv("path/to/file.csv")
3. ● Reading a JSON File: spark.read.json("path/to/file.json")
4. ● Reading a Parquet File: spark.read.parquet("path/to/file.parquet")
5. ● Writing DataFrame to CSV: df.write.csv("path/to/output")
6. ● Writing DataFrame to JSON: df.write.json("path/to/output")
7. ● Writing DataFrame to Parquet: df.write.parquet("path/to/output")
8. ● Show DataFrame: df.show()
9. ● Print Schema: df.printSchema()
10. ● Column Selection: df.select("column1", "column2")
11. ● Filtering Rows: df.filter(df["column"] > value)
12. ● Adding a New Column: df.withColumn("newColumn", expression)
13. ● Renaming a Column: df.withColumnRenamed("oldName", "newName")
14. ● Dropping a Column: df.drop("column")
15. ● Describe Data: df.describe().show()
16. ● Counting Rows: df.count()
17. ● Aggregating Data: df.groupBy("column").agg({"column": "sum"})
18. ● Sorting Data: df.sort("column")
19. ● Distinct Values: df.select("column").distinct()
● Sample Data: df.sample(withReplacement, fraction, seed)
2. Advanced Data Manipulation
● Joining DataFrames: df1.join(df2, "column")
● Union DataFrames: df1.union(df2)
● Pivot Table:
df.groupBy("column").pivot("pivot_column").agg(count("another_column"))
● Window Functions:
from pyspark.sql import Window;
windowSpec = Window.partitionBy("column")
● DataFrame to RDD Conversion: df.rdd
● RDD to DataFrame Conversion: rdd.toDF()
●
By: Girish Gowda
● Caching a DataFrame: df.cache()
● Uncaching a DataFrame: df.unpersist()
● Collecting Data to Driver: df.collect()
● Broadcast Join:
from pyspark.sql.functions import broadcast;
df1.join(broadcast(df2), "column")
3. Handling Missing and Duplicate Data
● Dropping Rows with Null Values: df.na.drop()
● Filling Null Values: df.na.fill(value)
● Dropping Duplicate Rows: df.dropDuplicates()
● Replacing Values: df.na.replace(["old_value"], ["new_value"])
4. Data Transformation
● UDF (User Defined Function):
from pyspark.sql.functions import udf;
udf_function = udf(lambda z: custom_function(z))
● String Operations:
from pyspark.sql.functions import lower, upper;
df.select(upper(df["column"]))
● Date and Time Functions:
from pyspark.sql.functions import current_date, current_timestamp;
df.select(current_date())
● Numeric Functions:
from pyspark.sql.functions import abs, sqrt;
df.select(abs(df["column"]))
● Conditional Expressions:
from pyspark.sql.functions import when;
df.select(when(df["column"] > value, "true").otherwise("false"))
● Type Casting: df.withColumn("column", df["column"].cast("new_type"))
● Explode Function (Array to Rows):
from pyspark.sql.functions import explode;
df.withColumn("exploded_column", explode(df["array_column"]))
● Pandas UDF:
from pyspark.sql.functions import pandas_udf;
@pandas_udf("return_type")
def pandas_function(col1, col2): return operation
●
By: Girish Gowda
● Aggregating with Custom Functions:
df.groupBy("column").agg(custom_agg_function(df["another_column"]))
● Window Functions (Rank, Lead, Lag):
from pyspark.sql.functions import rank, lead, lag;
windowSpec = Window.orderBy("column");
df.withColumn("rank", rank().over(windowSpec));
● Handling JSON Columns:
from pyspark.sql.functions import from_json, schema_of_json;
df.withColumn("parsed_json", from_json(df["json_column"], schema_of_json))
5. Data Profiling
● Column Value Counts: df.groupBy("column").count()
● Summary Statistics for Numeric Columns: df.describe()
Correlation Between Columns: df.stat.corr("column1", "column2")
● Crosstabulation and Contingency Tables:
df.stat.crosstab("column1","column2")
● Frequent Items in Columns: df.stat.freqItems(["column1", "column2"])
● Approximate Quantile Calculation:
df.approxQuantile("column", [0.25, 0.5,0.75], relativeError)
6. Data Visualization (Integration with other libraries)
● Convert to Pandas for Visualization: df.toPandas().plot(kind='bar')
● Histograms using Matplotlib: df.toPandas()["column"].hist()
● Box Plots using Seaborn: import seaborn as sns; sns.boxplot(x=df.toPandas()["column"])
● Scatter Plots using Matplotlib: df.toPandas().plot.scatter(x='col1', y='col2')
7. Data Import/Export
● Reading Data from JDBC Sources:
spark.read.format("jdbc").options(url="jdbc_url", dbtable="table_name").load()
● Writing Data to JDBC Sources:
df.write.format("jdbc").options(url="jdbc_url", dbtable="table_name").save()
● Reading Data from HDFS: spark.read.text("hdfs://path/to/file")
● Writing Data to HDFS: df.write.save("hdfs://path/to/output")
●
By: Girish Gowda
● Creating DataFrames from Hive Tables: spark.table("hive_table_name")
8. Working with Large Data
● Partitioning Data: df.repartition(numPartitions)
● Coalesce Partitions: df.coalesce(numPartitions)
● Reading Data in Chunks:
spark.read.option("maxFilesPerTrigger",1).csv("path/to/file.csv")
● Optimizing Data for Skewed Joins: df.repartition("skewed_column")
● Handling Data Skew in Joins: df1.join(df2.hint("broadcast"), "column")
9. Spark SQL
● Running SQL Queries on DataFrames:
df.createOrReplaceTempView("table");
spark.sql("SELECT * FROM table")
● Registering UDF for SQL Queries:
spark.udf.register("udf_name", lambda x: custom_function(x))
● Using SQL Functions in DataFrames:
from pyspark.sql.functions import expr;
df.withColumn("new_column", expr("SQL_expression"))
10. Machine Learning and Advanced Analytics
● VectorAssembler for Feature Vectors:
from pyspark.ml.feature import VectorAssembler;
assembler = VectorAssembler(inputCols=["col1", "col2"], outputCol="features")
● StandardScaler for Feature Scaling:
from pyspark.ml.feature import StandardScaler;
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures")
● Building a Machine Learning Pipeline:
from pyspark.ml import Pipeline; pipeline = Pipeline(stages=[assembler, scaler, ml_model])
● Train-Test Split: train, test = df.randomSplit([0.7, 0.3])
●
By: Girish Gowda
● Model Fitting and Predictions:
model = pipeline.fit(train);
predictions = model.transform(test)
● Cross-Validation for Model Tuning:
from pyspark.ml.tuning import CrossValidator;
crossval = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid)
● Hyperparameter Tuning:
from pyspark.ml.tuning import ParamGridBuilder; paramGrid =
ParamGridBuilder().addGrid(model.param, [value1, value2]).build()
11. Graph and Network Analysis
● Creating a GraphFrame:
from graphframes import GraphFrame;
g = GraphFrame(vertices_df, edges_df)
● Running Graph Algorithms: results = g.pageRank(resetProbability=0.15, maxIter=10)
● Subgraphs and Motif Finding: g.find("(a)-[e]->(b); (b)-[e2]->(a)")
12. Streaming Data Processing
● Reading from a Stream: spark.readStream.format("source").load()
● Writing to a Stream: query =df.writeStream.outputMode("append").format("console").start()
● Window Operations on Streaming Data:
df.groupBy(window(df["timestamp"], "1 hour")).count()
● Handling Late Data and Watermarks: df.withWatermark("timestamp", "2 hours")
● Triggering Streaming: df.writeStream.trigger(processingTime='1 hour').start()
● Streaming Aggregations: df.groupBy("group_column").agg({"value": "sum"})
● Reading from Kafka:
spark.readStream.format("kafka").option("kafka.bootstrap.servers",
"host:port").option("subscribe", "topic").load()
● Writing to Kafka:
df.writeStream.format("kafka").option("kafka.bootstrap.servers",
"host:port").option("topic", "topic").start()
●
By: Girish Gowda
13. Advanced Dataframe Transformations
● Handling Complex Data Types (Arrays, Maps):
df.selectExpr("explode(array_column) as value")
● Flattening Nested Structures: df.selectExpr("struct_col.*")
● Pivoting and Unpivoting Data:
df.groupBy("group_col").pivot("pivot_col").sum()
● Creating Buckets or Bins: from pyspark.ml.feature import Bucketizer; bucketizer =
Bucketizer(splits=[0, 10, 20], inputCol="feature", outputCol="bucketed_feature")
● Normalization of Data: from pyspark.ml.feature import Normalizer; normalizer =
Normalizer(inputCol="features", outputCol="normFeatures", p=1.0)
14. Data Quality and Validation
● Data Integrity Checks: df.checkpoint()
● Schema Validation: df.schema == expected_schema
● Data Completeness Validation: df.count() == expected_count
● Column Value Range Validation: df.filter((df["column"] >= lower_bound) & (df["column"] <=
upper_bound))
15. Integration with Other Data Systems
● Reading from Hive Tables: spark.sql("SELECT * FROM hive_table")
● Writing to Hive Tables: df.write.saveAsTable("hive_table")
● Connecting to External Databases: df.write.jdbc(url, table, properties)
● Using Hadoop File System (HDFS): df.write.save("hdfs://path/to/save")
By: Girish Gowda
16. Performance Tuning and Optimization
● Broadcast Variables for Join Optimization:
from pyspark.sql.functions import broadcast;
df1.join(broadcast(df2), "join_column")
● Caching Intermediate Data: df.cache()
● Repartitioning for Parallel Processing: df.repartition(num_partitions)
● Avoiding Shuffle and Spill to Disk: df.coalesce(num_partitions)
● Tuning Spark Configuration and Parameters:
spark.conf.set("spark.executor.memory", "2g")
17. Exploratory Data Analysis Techniques
● Histograms for Exploring Distributions:
df.select('column').rdd.flatMap(lambda x: x).histogram(10)
● Quantile and Percentile Analysis:
df.approxQuantile("column", [0.25, 0.5,0.75], 0.0)
● Exploring Data with Spark SQL: spark.sql("SELECT * FROM df_table").show()
● Calculating Covariance and Correlation:
df.stat.cov("col1", "col2"), df.stat.corr("col1", "col2")
18. Dealing with Different Data Formats
● Handling Avro Files:
df.write.format("avro").save("path"),spark.read.format("avro").load("path")
● Dealing with ORC Files: df.write.orc("path"), spark.read.orc("path")
● Handling XML Data: (Using spark-xml library)
● Dealing with Binary Files: spark.read.format("binaryFile").load("path")
By: Girish Gowda
19. Handling Geospatial Data
● Using GeoSpark for Geospatial Operations: (Integrating with GeoSpark
library)
● Geospatial Joins and Calculations: (Using location-based joins and UDFs
for distance calculations)
20. Time Series Data Handling
● Time Series Resampling and Aggregation:
df.groupBy(window(df["timestamp"], "1 hour")).agg({"value": "mean"})
● Time Series Window Functions:
from pyspark.sql.functions import window;
df.groupBy(window("timestamp", "1 hour")).mean()
21. Advanced Machine Learning Operations
● Custom Machine Learning Models with MLlib:
from pyspark.ml.classification import LogisticRegression; lr = LogisticRegression()
● Text Analysis with MLlib:
from pyspark.ml.feature import Tokenizer;
tokenizer = Tokenizer(inputCol="text", outputCol="words")
● Model Evaluation and Metrics:
from pyspark.ml.evaluation import BinaryClassificationEvaluator;
evaluator = BinaryClassificationEvaluator()
● Model Persistence and Loading: model.save("path"), ModelType.load("path")
22. Graph Analysis with GraphFrames
● Creating GraphFrames for Network Analysis:
from graphframes import GraphFrame;
g = GraphFrame(vertices_df, edges_df)
● Running Graph Algorithms (e.g., PageRank, Shortest Paths):
g.pageRank(resetProbability=0.15, maxIter=10)
By: Girish Gowda
23. Custom Transformation and UDFs
● Applying Custom Transformations: df.withColumn("custom_col", custom_udf("column"))
● Vector Operations for ML Features:
from pyspark.ml.linalg import Vectors;
df.withColumn("vector_col", Vectors.dense("column"))
24. Logging and Monitoring
● Logging Operations in Spark: spark.sparkContext.setLogLevel("WARN")
25. Best Practices and Patterns
● Following Data Partitioning Best Practices: (Optimizing partition
strategy for data size and operations)
● Efficient Data Serialization: (Using Kryo serialization for performance)
● Optimizing Data Locality: (Ensuring data is close to computation
resources)
● Error Handling and Recovery Strategies: (Implementing try-catch logic and
checkpointing)
26. Security and Compliance
● Data Encryption and Security: (Configuring Spark with encryption and
security features)
● GDPR Compliance and Data Anonymization: (Implementing data masking and
anonymization)
27. Advanced Data Science Techniques
● Deep Learning Integration (e.g., with TensorFlow): (Using Spark with
TensorFlow for distributed deep learning)
● Complex Event Processing in Streams: (Using structured streaming for
event pattern detection)
By: Girish Gowda
28. Cloud Integration
● Running Spark on Cloud Platforms (e.g., AWS, Azure, GCP): (Setting up
Spark clusters on cloud services)
● Integrating with Cloud Storage Services: (Reading and writing data to
cloud storage like S3, ADLS, GCS)
df.groupBy(window(df["timestamp"], "1
hour")).count()
● Handling Late Data and Watermarks:
df.withWatermark("timestamp", "2 hours")
● Triggering Streaming:
df.writeStream.trigger(processingTime='1 hour').start()
● Streaming Aggregations:
df.groupBy("group_column").agg({"value": "sum"})
● Reading from Kafka:
spark.readStream.format("kafka").option("kafka.bootstrap.servers",
"host:port").option("subscribe",
"topic").load()
● Writing to Kafka:
df.writeStream.format("kafka").option("kafka.bootstrap.servers",
"host:port").option("topic",
"topic").start()
13. Advanced Dataframe Transformations
● Handling Complex Data Types (Arrays, Maps):
df.selectExpr("explode(array_column) as value")
● Flattening Nested Structures:
df.selectExpr("struct_col.*")
● Pivoting and Unpivoting Data:
df.groupBy("group_col").pivot("pivot_col").sum()
● Creating Buckets or Bins: from pyspark.ml.feature import
Bucketizer; bucketizer =
Bucketizer(splits=[0, 10, 20], inputCol="feature",
outputCol="bucketed_feature")
● Normalization of Data: from pyspark.ml.feature import
Normalizer; normalizer =
Normalizer(inputCol="features",
outputCol="normFeatures", p=1.0)
14. Data Quality and Validation
● Data Integrity Checks: df.checkpoint()
● Schema Validation: df.schema == expected_schema
● Data Completeness Validation: df.count() == expected_count
● Column Value Range Validation:
df.filter((df["column"] >= lower_bound) &
(df["column"] <=
upper_bound))
15. Integration with Other Data Systems
● Reading from Hive Tables: spark.sql("SELECT * FROM
hive_table")
● Writing to Hive Tables:
df.write.saveAsTable("hive_table")
● Connecting to External Databases: df.write.jdbc(url,
table, properties)
● Using Hadoop File System (HDFS):
df.write.save("hdfs://path/to/save")
By: Girish Gowda
16. Performance Tuning and Optimization
● Broadcast Variables for Join Optimization:
from pyspark.sql.functions import broadcast;
df1.join(broadcast(df2), "join_column")
● Caching Intermediate Data: df.cache()
● Repartitioning for Parallel Processing:
df.repartition(num_partitions)
● Avoiding Shuffle and Spill to Disk:
df.coalesce(num_partitions)
● Tuning Spark Configuration and Parameters:
spark.conf.set("spark.executor.memory",
"2g")
17. Exploratory Data Analysis Techniques
● Histograms for Exploring Distributions:
df.select('column').rdd.flatMap(lambda x: x).histogram(10)
● Quantile and Percentile Analysis:
df.approxQuantile("column", [0.25, 0.5,0.75], 0.0)
● Exploring Data with Spark SQL: spark.sql("SELECT *
FROM df_table").show()
● Calculating Covariance and Correlation:
df.stat.cov("col1", "col2"),
df.stat.corr("col1", "col2")
18. Dealing with Different Data Formats
● Handling Avro Files:
df.write.format("avro").save("path"),spark.read.format("avro").load("path")
● Dealing with ORC Files: df.write.orc("path"),
spark.read.orc("path")
● Handling XML Data: (Using spark-xml library)
● Dealing with Binary Files:
spark.read.format("binaryFile").load("path")
By: Girish Gowda
19. Handling Geospatial Data
● Using GeoSpark for Geospatial Operations: (Integrating
with GeoSpark
library)
● Geospatial Joins and Calculations: (Using location-based
joins and UDFs
for distance calculations)
20. Time Series Data Handling
● Time Series Resampling and Aggregation:
df.groupBy(window(df["timestamp"],
"1 hour")).agg({"value": "mean"})
● Time Series Window Functions:
from pyspark.sql.functions import window;
df.groupBy(window("timestamp", "1
hour")).mean()
21. Advanced Machine Learning Operations
● Custom Machine Learning Models with MLlib:
from pyspark.ml.classification import LogisticRegression; lr
= LogisticRegression()
● Text Analysis with MLlib:
from pyspark.ml.feature import Tokenizer;
tokenizer = Tokenizer(inputCol="text",
outputCol="words")
● Model Evaluation and Metrics:
from pyspark.ml.evaluation import
BinaryClassificationEvaluator;
evaluator = BinaryClassificationEvaluator()
● Model Persistence and Loading:
model.save("path"), ModelType.load("path")
22. Graph Analysis with GraphFrames
● Creating GraphFrames for Network Analysis:
from graphframes import GraphFrame;
g = GraphFrame(vertices_df, edges_df)
● Running Graph Algorithms (e.g., PageRank, Shortest Paths):
g.pageRank(resetProbability=0.15, maxIter=10)
By: Girish Gowda
23. Custom Transformation and UDFs
● Applying Custom Transformations:
df.withColumn("custom_col", custom_udf("column"))
● Vector Operations for ML Features:
from pyspark.ml.linalg import Vectors;
df.withColumn("vector_col",
Vectors.dense("column"))
24. Logging and Monitoring
● Logging Operations in Spark:
spark.sparkContext.setLogLevel("WARN")
25. Best Practices and Patterns
● Following Data Partitioning Best Practices: (Optimizing
partition
strategy for data size and operations)
● Efficient Data Serialization: (Using Kryo serialization
for performance)
● Optimizing Data Locality: (Ensuring data is close to
computation
resources)
● Error Handling and Recovery Strategies: (Implementing
try-catch logic and
checkpointing)
26. Security and Compliance
● Data Encryption and Security: (Configuring Spark with
encryption and
security features)
● GDPR Compliance and Data Anonymization: (Implementing data
masking and
anonymization)
27. Advanced Data Science Techniques
● Deep Learning Integration (e.g., with TensorFlow): (Using
Spark with
TensorFlow for distributed deep learning)
● Complex Event Processing in Streams: (Using structured
streaming for
event pattern detection)
By: Girish Gowda
28. Cloud Integration
● Running Spark on Cloud Platforms (e.g., AWS, Azure, GCP):
(Setting up
Spark clusters on cloud services)
● Integrating with Cloud Storage Services: (Reading and
writing data to
cloud storage like S3, ADLS, GCS)
