Metadata Capture

Atlas works seamlessly with various data platforms and includes custom hooks for data movement tools. A bespoke Atlas hook can be developed to capture metadata events when data moves from the FTP server to S3. Furthermore, Atlas's integration with Hive and Spark will gather lineage information during data processing.

The Genesis of Apache Atlas

Apache Atlas's journey began in 2012, initiated by engineers at LinkedIn who recognized the need for robust data governance in their big data framework. The project reached top-level Apache Software Foundation (ASF) status in 2015.

Constructing the Lineage Graph

Atlas processes the captured metadata events to create a detailed data lineage graph. This visual representation will illustrate:

• The flow of data from the FTP server to S3
• The processing steps within Spark
• The loading of the transformed data into Postgres

Deploying Apache Atlas on AWS

Although Apache Atlas is not an official AWS service, there are several methods to deploy it on AWS:

1. EMR with Managed Hadoop: Amazon EMR provides a managed Hadoop cluster option with HBase, Hive, and Zookeeper, essential for Atlas. Following the official Atlas installation guide (https://atlas.apache.org/1.0.0/InstallationSteps.html) can help set up Atlas on your EMR cluster.
2. EC2 with Manual Installation: You can create EC2 instances and manually set up the required software (HBase, Hive, Zookeeper, Atlas) to configure your own Hadoop environment. This route offers greater flexibility but demands more technical skills for setup and upkeep.
3. Managed Services with Atlas Integration: Various managed services on AWS offer integrations with Apache Atlas. For example, Cloudera provides a managed Hadoop service on AWS (Cloudera Data Flow) that includes Atlas integration. Investigate managed services that meet your specific requirements and check for built-in Atlas integrations.

Querying Lineage

Atlas features a user-friendly interface and REST APIs for querying the data lineage graph. Users can delve into the lineage of specific data points within the Postgres database. For instance, if one needs to trace the origin and transformations of a particular data element in Postgres, Atlas enables querying of the lineage graph to visualize the complete journey from the FTP server.

Sample Code (Illustrative Examples)

#### Custom Atlas Hook for Capturing FTP to S3 Lineage (Python):

from atlasclient import AtlasClient

def capture_ftp_to_s3_lineage(ftp_path, s3_path):

client = AtlasClient("http://localhost:21000")

client.create_entity(type_name="custom_data_movement", qualified_name=f"ftp_to_s3_{ftp_path}")

client.create_lineage(s3_path, f"ftp_to_s3_{ftp_path}", "MOVED_FROM")

# Simulate data movement from FTP to S3 ftp_path = "/data/sales.csv" s3_path = "s3://mybucket/sales.csv" capture_ftp_to_s3_lineage(ftp_path, s3_path)

#### Spark Job Lineage with Atlas (Simplified Spark Scala):

from pyspark.sql import SparkSession from atlasclient import AtlasClient # Ensure Atlas client library is installed

# Create a SparkSession spark = SparkSession.builder.appName("SparkLineageExample").getOrCreate()

# Read data from S3 data = spark.read.csv("s3://mybucket/sales.csv")

# Perform data transformations (replace with actual transformations) transformed_data = data.filter(data.col("sales_amount") > 1000)

# Capture lineage between S3 data and Spark table (assuming stored in Hive metastore) atlas_client = AtlasClient("http://localhost:21000") # Replace with your Atlas server URL atlas_client.create_entity(type_name="hive_table", qualified_name="sales_data_processed") atlas_client.create_lineage("sales_data_processed", "s3://mybucket/sales.csv", "TRANSFORMED_FROM")

# Write transformed data to Postgres transformed_data.write.format("jdbc")

.option("url", "jdbc:postgresql://localhost:5432/mydatabase")

.option("dbtable", "sales_processed")

.save()

# Capture lineage between Spark table and Postgres table atlas_client.create_lineage("public.sales_processed", "sales_data_processed", "LOADED_FROM")

# Stop the SparkSession spark.stop()

Explanation:

1. Necessary libraries are imported: SparkSession for handling Spark DataFrames and AtlasClient to communicate with Apache Atlas.
2. An instance of SparkSession is created.
3. Data is read from S3 using Spark’s read.csv method.
4. Replace the placeholder data transformation with your actual processing logic.
5. An Atlas client instance is created to interact with your Atlas server.
6. Lineage is captured between the S3 data and the Spark table (assuming the Spark tables are stored in the Hive metastore) by creating an entity for the Spark table and establishing lineage with the S3 path.
7. The transformed data is written to a Postgres table using Spark’s JDBC write functionality.
8. Lineage is captured between the Spark table and the Postgres table via the Atlas client.
9. Finally, the SparkSession is stopped.

Custom Atlas Hook for Capturing Spark to Postgres Lineage (Python):

from pyspark.sql import SparkSession from atlasclient import AtlasClient # Ensure Atlas client library is installed

# Create a SparkSession spark = SparkSession.builder.appName("SparkLineageExample").getOrCreate()

# Read data from S3 data = spark.read.csv("s3://mybucket/sales.csv")

# Perform data transformations (replace with actual transformations) transformed_data = data.filter(data.col("sales_amount") > 1000)

# Capture lineage between S3 data and Spark table (assuming stored in Hive metastore) atlas_client = AtlasClient("http://localhost:21000") # Replace with your Atlas server URL atlas_client.create_entity(type_name="hive_table", qualified_name="sales_data_processed") atlas_client.create_lineage("sales_data_processed", "s3://mybucket/sales.csv", "TRANSFORMED_FROM")

# Write transformed data to Postgres transformed_data.write.format("jdbc")

.option("url", "jdbc:postgresql://localhost:5432/mydatabase")

.option("dbtable", "sales_processed")

.save()

# Capture lineage between Spark table and Postgres table atlas_client.create_lineage("public.sales_processed", "sales_data_processed", "LOADED_FROM")

# Stop the SparkSession spark.stop()

Explanation:

1. Import necessary libraries: SparkSession for working with Spark DataFrames and AtlasClient for interactions with Apache Atlas.
2. Create a SparkSession instance.
3. Read data from S3 using Spark’s read.csv method.
4. Substitute the placeholder data transformation with your actual logic.
5. Create an Atlas client instance for communication with your Atlas server.
6. Capture lineage between the S3 data and the Spark table (assuming Spark tables are stored in the Hive metastore) by creating an entity for the Spark table and establishing lineage with the S3 path.
7. Write the transformed data into a Postgres table using Spark’s JDBC write capabilities.
8. Capture lineage between the Spark table and the Postgres table through the Atlas client.
9. Finally, terminate the SparkSession.

Remember:

• Substitute placeholder values like “http://localhost:21000" with your actual Atlas server URL.
• Adjust the Spark code and Atlas calls to fit your specific data processing requirements and selected data movement tools.
• Additional Atlas integrations may be necessary based on your data pipeline (e.g., Sqoop for transferring data from S3 to Hive).

Conclusion

By incorporating Apache Atlas into your data pipeline that includes FTP, S3, Spark processing, and Postgres, you can achieve valuable insights into your data's lineage. This level of transparency enhances data governance, facilitates debugging, and supports improved data-driven decision-making. As your data ecosystem continues to grow, ensure to adapt your Atlas integrations to fully capture data lineage for effective data management.