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Batch Data Ingestion

Here are some batch data ingestion methods in Spark.

File Based Ingestion

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File-based ingestion refers to the process of importing data from various file formats into Apache Spark.

CSV

df_csv = spark.read.csv("file.csv", header=True)
df_csv.show()

df_json = spark.read.json("file.json")
df_json.show()

Parquet

df_parquet = spark.read.parquet("file.parquet")
df_parquet.show()

cXML

df_xml = spark.read.format("com.databricks.spark.xml").option("rowTag", "record").load("file.xml")
df_xml.show()

Replication & ETL

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You can leverage database replication and ETL processes to facilitate continuous data ingestion and transformation.

Database Replication

# Assuming df_db is the DataFrame from the replicated database
df_db.write.mode("append").saveAsTable("database_table")

ETL Process

# Assuming df_raw is the raw data DataFrame
df_transformed = df_raw.select("col1", "col2").withColumn("new_col", expr("col1 + col2"))
df_transformed.write.mode("append").saveAsTable("transformed_table")

Import from API

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Integrating external APIs seamlessly allows you to efficiently retrieve and ingest data.

HTTP API

import requests
response = requests.get("https://api.example.com/data")
json_data = response.json()
df_api = spark.read.json(spark.sparkContext.parallelize([json_data]))
df_api.show()

Data Transfer Protocols

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Data transfer protocols like File Transfer Protocol (FTP), Secure File Transfer Protocol (SFTP), and Hypertext Transfer Protocol (HTTP) are essential for efficient and secure data transfer.

FTP Ingestion

spark.read.format("com.springml.spark.sftp").option("host", "ftp.example.com").load("/path/to/file.csv")

HTTP Ingestion

spark.read.text("http://example.com/data.txt")

Source Assessment

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You must evaluate data source characteristics and quality, as they are fundamental for effective data integration.

Characteristics Assessment

df_source.describe().show()

Quality Assessment

df_source.selectExpr("count(distinct *) as unique_records").show()

Schema Mapping

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Mapping and transforming data schemas can be a challenge for you, but at the same time, they are essential for fitting data into the target model.

Schema Mapping

df_mapped = df_raw.selectExpr("col1 as new_col1", "col2 as new_col2")

Schema Transformation

df_transformed = df_mapped.withColumn("new_col3", expr("new_col1 + new_col2"))

Data Validation

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You must ensure high data quality during ingestion, as it is critical for downstream processes.

Data Validation

df_validated = df_raw.filter("col1 IS NOT NULL AND col2 > 0")

Data Cleansing

df_cleansed = df_raw.na.fill(0, ["col1"])

Data deduplication

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You must prevent duplicate records during ingestion, as it is vital to maintain data integrity downstream.

Remove Duplicates

df_deduplicated = df_raw.dropDuplicates(["col1", "col2"])

Unstructured Data

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Unstructured data includes documents, images, logs, and more. Techniques for ingesting and extracting insights are crucial.

NLP

# Using Spark NLP library for text data processing
df_text = spark.read.text("text_file.txt")

Governance & Compliance

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You should ensure data governance practices and compliance with regulatory requirements and data privacy laws, as these are essential for responsible data handling.

Compliance Checks

# Example: Ensure GDPR compliance
df_gdpr_compliant = df_raw.filter("country IN ('EU')")