big-data

name	big-data
description	Techniques and frameworks for processing large-scale datasets that exceed the capacity of traditional database systems, including distributed computing, parallel processing, and scalable data pipelines.
category	data-science
keywords	["big-data","distributed computing","apache spark","hadoop","dask","data pipelines","scalability","parallel processing","petabyte-scale"]
difficulty	advanced
related_skills	["pandas","etl-pipelines","data-visualization"]

Big Data

What I do

I provide capabilities for processing and analyzing datasets that are too large for traditional tools. I enable distributed computing across clusters, parallel processing of massive datasets, and scalable data pipelines. I help you work with data ranging from gigabytes to petabytes using frameworks that can scale from a single machine to thousands of nodes.

When to use me

• Processing datasets larger than available RAM (typically >10GB)
• Building ETL pipelines that run on scheduled intervals
• Working with distributed file systems (HDFS, S3, GCS)
• Running machine learning at scale
• Processing streaming data in real-time
• Aggregating and analyzing log files
• Building data lakes and warehouses
• Parallelizing computations across multiple cores or machines

Core Concepts

Distributed Computing

• Data Partitioning: Splitting data across multiple nodes
• Parallel Execution: Processing partitions simultaneously
• Fault Tolerance: Recovering from node failures
• Data Locality: Processing where data resides to minimize transfer

Big Data Frameworks

• Apache Spark: In-memory distributed computing engine
• Dask: Parallel computing library for Python
• Apache Hadoop: HDFS + MapReduce framework
• Apache Flink: Stream processing framework

Storage Systems

• HDFS: Hadoop Distributed File System
• Object Storage: S3, GCS, Azure Blob
• Columnar Formats: Parquet, ORC (optimized for analytics)

Processing Paradigms

• Batch Processing: Process complete datasets periodically
• Stream Processing: Process data in real-time as it arrives
• Lambda Architecture: Combine batch and stream processing
• Kappa Architecture: Stream-only approach

Code Examples (Python)

# Using Apache Spark
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, max, min, count
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType
from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.regression import LinearRegression

# Initialize Spark
spark = SparkSession.builder \
    .appName("BigDataProcessing") \
    .config("spark.driver.memory", "16g") \
    .config("spark.sql.shuffle.partitions", "200") \
    .getOrCreate()

# Create DataFrame from various sources
# From CSV
df = spark.read.csv("s3://bucket/path/*.csv", 
                    header=True, 
                    inferSchema=True)

# From Parquet
df = spark.read.parquet("s3://bucket/data/")

# From HDFS
df = spark.read.format("orc").load("hdfs://namenode:9000/data/")

# From RDBMS with partitioning
df = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:postgresql://host:5432/db") \
    .option("dbtable", "large_table") \
    .option("partitionColumn", "id") \
    .option("lowerBound", ) \
    .option(, ) \
    .option(, ) \
    .load()


result = df.(col() > ) \
    .groupBy() \
    .agg(
        avg().alias(),
        count().alias(),
        ().alias()
    ) \
    .orderBy(col().desc())


df_filtered = df.(col() == ).cache()


result.write.mode().parquet()
result.write.mode().partitionBy(, ).parquet()


feature_cols = [, , , ]
assembler = VectorAssembler(inputCols=feature_cols, outputCol=)
scaler = StandardScaler(inputCol=, outputCol=)
lr = LinearRegression(featuresCol=, labelCol=)

pipeline = Pipeline(stages=[assembler, scaler, lr])
model = pipeline.fit(train_df)
predictions = model.transform(test_df)


 dask.dataframe  dd
 dask.distributed  Client


client = Client(n_workers=, threads_per_worker=, memory_limit=)


ddf = dd.from_pandas(pandas_df, npartitions=)


ddf = dd.read_parquet(, 
                       columns=[, , ])


result = ddf.groupby().agg({: [, , ]})


pandas_result = result.compute()


ddf = dd.read_csv(, 
                  dtype={: , : },
                  parse_dates=[])


ddf[] = ddf[].apply( x: complex_function(x), meta=(col1, ))


rolling_mean = ddf[].rolling(window=).mean().compute()


 dask.db  read_sql_query
ddf = read_sql_query(, 
                      index_col=,
                      divisions=[, , , ],
                      npartitions=)


 ():
    
     df.describe()

results = ddf.map_partitions(process_partition)


 vaex


df = vaex.()


df_summary = df.groupby(, agg=[, ])


df[] = np.log(df[])


df_sample = df.sample(n=)


df_filtered = df[(df[] > ) & (df[] > )]
df_filtered.export_parquet()


 polars  pl


df = pl.read_parquet()


result = df.group_by().agg([
    pl.col().mean().alias(),
    pl.col().().alias()
])


lazy_df = pl.scan_parquet()
result = lazy_df.(pl.col() > ) \
    .group_by() \
    .agg(pl.col().mean()) \
    .collect()

Best Practices

1. Choose the right tool: Use Dask for scaling Python workflows, Spark for enterprise scale, Polars for single-machine speed.

2. Partition data strategically: Partition by columns used for filtering to enable partition pruning.

3. Minimize shuffles: Group and reduce operations before wide transformations.

4. Use appropriate file formats: Parquet for analytics (columnar, compressed), ORC for Hive.

5. Cache frequently accessed data: Use .cache() or .persist() for reused DataFrames.

6. Tune parallelism: Adjust partition counts based on cluster resources and data size.

7. Monitor and profile: Use Spark UI, Dask dashboard to identify bottlenecks.

8. Handle skewed data: Repartition or use salting for highly skewed keys.

Common Patterns

Pattern 1: ETL Pipeline with Spark

def etl_pipeline(spark, input_path, output_path):
    # Read raw data
    raw_df = spark.read.json(input_path)
    
    # Apply schema
    schema = StructType([
        StructField("id", StringType(), True),
        StructField("timestamp", TimestampType(), True),
        StructField("value", DoubleType(), True),
        StructField("status", StringType(), True)
    ])
    df = spark.createDataFrame(raw_df.rdd, schema)
    
    # Clean and transform
    df_clean = df \
        .filter(col("value").isNotNull()) \
        .withColumn("date", col("timestamp").cast("date")) \
        .withColumn("hour", hour(col("timestamp"))) \
        .withColumn("value_normalized", 
                   (col("value") - col("value").mean()) / col("value").std())
    
    # Aggregate by time windows
    df_agg = df_clean.groupBy(
        window(col("timestamp"), "1 hour"),
        col("status")
    ).agg(
        count("*").alias("count"),
        avg("value").alias("avg_value"),
        min("value").alias("min_value"),
        max("value").alias("max_value")
    )
    
    # Write with partitioning
    df_agg.write \
        .mode("append") \
        .partitionBy("date") \
        .parquet(output_path)
    
     df_agg

Pattern 2: Incremental Processing with Dask

def incremental_process(base_path, new_data_path, checkpoint_path):
    import os
    import dask.dataframe as dd
    from dask.delayed import delayed
    
    # Check for checkpoint
    if os.path.exists(checkpoint_path):
        base_df = dd.read_parquet(checkpoint_path)
    else:
        base_df = dd.from_pandas(pd.DataFrame(), npartitions=1)
    
    # Read new data
    new_df = dd.read_parquet(new_data_path)
    
    # Append new data
    combined_df = dd.concat([base_df, new_df], ignore_index=True)
    
    # Process
    processed_df = combined_df.groupby("category").agg({
        "value": "sum",
        "timestamp": "max"
    })
    
    # Write checkpoint
    processed_df.to_parquet(checkpoint_path, overwrite=True)
    
    return processed_df

Pattern 3: Distributed ML with Spark

def distributed_ml_pipeline(spark, train_path, test_path):
    from pyspark.ml.feature import StringIndexer, VectorAssembler
    from pyspark.ml.classification import RandomForestClassifier
    from pyspark.ml import Pipeline
    
    # Read data
    train_df = spark.read.parquet(train_path)
    test_df = spark.read.parquet(test_path)
    
    # Feature engineering pipeline
    categorical_cols = ["cat1", "cat2", "cat3"]
    numerical_cols = ["num1", "num2", "num3", "num4"]
    
    # Index categorical features
    indexers = [StringIndexer(inputCol=c, outputCol=c+"_index", handleInvalid="keep")
                for c in categorical_cols]
    
    # Assemble features
    assembler = VectorAssembler(
        inputCols=[c+"_index" for c in categorical_cols] + numerical_cols,
        outputCol="features"
    )
    
    # Model
    rf = RandomForestClassifier(
        featuresCol="features",
        labelCol="label",
        numTrees=100,
        maxDepth=10,
        numPartitions=100
    )
    
    # Build pipeline
    pipeline = Pipeline(stages=indexers + [assembler, rf])
    
    # Cross-validation
    from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
    param_grid = ParamGridBuilder() \
        .addGrid(rf.numTrees, [50, 100, ]) \
        .addGrid(rf.maxDepth, [, , ]) \
        .build()
    
    cv = CrossValidator(
        estimator=pipeline,
        estimatorParamMaps=param_grid,
        evaluator=BinaryClassificationEvaluator(),
        numFolds=
    )
    
    
    cv_model = cv.fit(train_df)
    
    
    predictions = cv_model.transform(test_df)
    
    
    evaluator = BinaryClassificationEvaluator(labelCol=, metricName=)
    auc = evaluator.evaluate(predictions)
    
     cv_model, predictions, auc