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Let's quickly learn about Databricks, Lakehouse architecture and their integration with cloud service providers : What is Databricks? Databricks is a cloud-based data engineering platform that provides a unified analytics platform for data engineering, data science and data analytics. It's built on top of Apache Spark and supports various data sources, processing engines and data science frameworks. What is Lakehouse Architecture? Lakehouse architecture is a modern data architecture that combines the benefits of data lakes and data warehouses. It provides a centralized repository for storing and managing data in its raw, unprocessed form, while also supporting ACID transactions, schema enforcement and data governance. Key components of Lakehouse architecture: Data Lake: Stores raw, unprocessed data. Data Warehouse: Supports processed and curated data for analytics. Metadata Management: Tracks data lineage, schema and permissions. Data Governance: Ensures data quality, security ...

  How to do an incremental load in ADF? Incremental loading in Azure Data Factory (ADF) involves loading only the data that has changed since the last load. This can be achieved by using a combination of source system change tracking mechanisms (like timestamps or change data capture) and lookup activities in ADF pipelines to identify new or updated data. What is data profiling? Data profiling is the process of analyzing and understanding the structure, content, quality, and relationships within a dataset. It involves examining statistics, patterns, and anomalies to gain insights into the data and ensure its suitability for specific use cases like reporting, analytics, or machine learning. Difference between ETL and ELT? ETL (Extract, Transform, Load) involves extracting data from source systems, transforming it into a suitable format, and then loading it into a target system. ELT (Extract, Load, Transform) involves loading raw data into a target system first, then transforming it ...

AWS S3 (Simple Storage Service): Amazon Simple Storage Service (Amazon S3) is a scalable object storage service offered by Amazon Web Services (AWS). It provides developers and IT teams with secure, durable, and highly available storage infrastructure for a wide range of use cases, including data backup and recovery, data archiving, web and mobile applications, big data analytics, and content distribution. Key Features: 1. Scalability: Amazon S3 is designed to scale seamlessly from a few gigabytes to petabytes or more of data without any upfront provisioning. It can handle virtually unlimited amounts of data and requests. 2. Durability and Availability: S3 stores data redundantly across multiple devices and facilities within a region to ensure high durability and availability. It offers 99.999999999% (11 nines) durability and 99.99% availability SLA. 3. Security: S3 provides several security features to protect data at rest and in transit, including server-side encryption, encryption i...

In #azuredatafactory at #transform and #enrich part can be done automatically or manually written by #pyspark two examples below one data source #csv another is #sqlserver with #incrementalloading Below is a simple end-to-end PySpark code example for a transform and enrich process in Azure Databricks. This example assumes you have a dataset stored in Azure Blob Storage, and you're using Azure Databricks for processing. ```python # Import necessary libraries from pyspark.sql import SparkSession from pyspark.sql.functions import col, lit, concat # Initialize SparkSession spark = SparkSession.builder \     .appName("Transform and Enrich Process") \     .getOrCreate() # Read data from Azure Blob Storage df = spark.read.csv("wasbs://<container_name>@<storage_account>.blob.core.windows.net/<file_path>", header=True) # Perform transformations transformed_df = df.withColumn("new_column", col("old_column") * 2) # Enrich data enriched...

  pexel Let first discuss what is incremental loading into the data warehouse by ETL from different data sources including databases. Incremental Loading into Data Warehouses: Incremental loading is crucial for efficiently updating data warehouses without reprocessing all data. It involves adding only new or modified data since the last update. Key aspects include: 1. Efficiency: Incremental loading reduces processing time and resource usage by only handling changes. 2. Change Detection: Techniques like timestamp comparison or change data capture (CDC) identify modified data. 3. Data Consistency: Ensure consistency by maintaining referential integrity during incremental updates. 4. Performance: Proper indexing, partitioning, and parallel processing enhance performance during incremental loads. 5. Logging and Auditing: Logging changes ensures traceability and facilitates error recovery in incremental loading processes. Incremental Loading Explained In contrast to a full load,...