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 within the target system. The main difference lies in when the transformation occurs, with ETL performing transformations before loading data into the target, while ELT performs transformations after loading data into the target.
Difference between data lake and delta lake?
A data lake is a centralized repository that allows storage of structured, semi-structured, and unstructured data at any scale. Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. Delta Lake adds reliability to data lakes by providing features like ACID transactions, schema enforcement, and time travel capabilities.
Azure blob vs Azure ADLS gen2?
Azure Blob Storage is a scalable object storage service for unstructured data. Azure Data Lake Storage Gen2 (ADLS Gen2) is a hierarchical file system built on top of Blob Storage, offering capabilities like directory structure, file-level security, and optimized performance for big data analytics workloads.
Can we call a pipeline iteratively in ADF?
Azure Data Factory does not have built-in support for iterative execution of pipelines. However, you can achieve iterative execution by using a combination of looping constructs (like ForEach) and conditional logic within your pipeline or orchestrating tool.
How can you ingest and store on-premise data into Azure Blob Storage?
You can ingest on-premise data into Azure Blob Storage using various methods such as Azure Data Factory, Azure Storage Explorer, Azure CLI, AzCopy, or PowerShell scripts. These tools provide different ways to transfer data securely from on-premise systems to Azure Blob Storage.
What are Indexes?
Indexes are data structures associated with database tables that improve the speed of data retrieval operations. They allow for faster lookup of rows based on the values of certain columns, reducing the need for scanning the entire table.
What Azure Key Vault is used?
Azure Key Vault is used to securely store and manage sensitive information such as cryptographic keys, passwords, certificates, and secrets. It provides centralized management of keys and secrets used by cloud applications and services.
What is list comprehension?
List comprehension is a concise way of creating lists in Python by combining a for loop and an optional condition into a single line of code. It provides a more readable and compact syntax for generating lists compared to traditional loops.
What is map function?
The map function in Python is used to apply a specified function to each item in an iterable (such as a list) and return a new iterable containing the results. It allows for efficient and concise transformation of data without the need for explicit loops.
What are transforms and what are actions in Spark?
In Spark, transformations are operations that create new RDDs (Resilient Distributed Datasets) from existing ones, while actions are operations that trigger the execution of Spark transformations and return results to the driver program or write data to external storage.
What is Lazy Evaluation?
Lazy evaluation is a programming paradigm where the evaluation of an expression is deferred until its value is actually needed. In Spark, transformations are lazily evaluated, meaning they are not executed immediately but instead build up a directed acyclic graph (DAG) of operations that are executed only when an action is called.
What is Spark Context?
Spark Context is the main entry point for Spark functionality in a Spark application. It represents a connection to a Spark cluster and is used to create RDDs, broadcast variables, and accumulators, as well as to control various Spark configurations.
Difference between pandas DataFrame and PySpark DataFrame?
Pandas DataFrame is a data structure in Python used for data manipulation and analysis, primarily for small to medium-sized datasets that fit into memory. PySpark DataFrame is similar to Pandas DataFrame but is distributed across multiple nodes in a Spark cluster, allowing for scalable processing of large datasets.
Work with Streams? How Streams can be processed?
Streams are continuous sequences of data elements that can be processed in real-time. In platforms like Apache Kafka or Azure Event Hubs, streams can be processed using stream processing frameworks like Apache Spark Structured Streaming or Azure Stream Analytics. These frameworks allow for the transformation, aggregation, and analysis of streaming data in near real-time.
How to connect ADF with Data Governance tools?
Azure Data Factory can be integrated with Data Governance tools through custom activities, REST API calls, or Azure Logic Apps. By leveraging these integration points, you can automate metadata management, data lineage tracking, data quality monitoring, and compliance enforcement within your data pipelines.
Moving sum partition by group?
A moving sum partition by group involves calculating the sum of a specified column over a sliding window of rows within each group in a dataset. This can be achieved using window functions in SQL or by using libraries like Pandas or PySpark in Python.
Why Parquet is used by a lot of systems?
Parquet is a columnar storage format optimized for big data analytics workloads. It offers efficient compression, columnar storage, and support for complex nested data structures, making it well-suited for query performance, storage efficiency, and compatibility with various processing frameworks like Apache Spark and Apache Hive.
Difference between repartition and coalesce?
Repartition and coalesce are both methods used to control the partitioning of data in Spark RDDs or DataFrames. Repartition involves reshuffling data across partitions to achieve a specified number of partitions, potentially resulting in data movement across the cluster. Coalesce, on the other hand, reduces the number of partitions without a full shuffle, usually resulting in fewer stages of data movement.
What is CTE?
CTE stands for Common Table Expression. It is a temporary named result set that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement. CTEs improve readability and maintainability of complex SQL queries by allowing for the modularization of subqueries.
Difference between delete and truncate?
Delete is a DML (Data Manipulation Language) operation used to remove rows from a table based on a specified condition, allowing for selective deletion of data. Truncate is a DDL (Data Definition Language) operation used to remove all rows from a table, effectively resetting the table to an empty state without logging individual row deletions.
What are Delta tables and how are they advantageous to data frames?
Delta tables are a type of table format in Delta Lake that brings ACID transactions, schema enforcement, and time travel capabilities to data lakes. They provide reliability and performance optimizations for big data workloads, making them advantageous to data frames by ensuring data consistency, enabling efficient data manipulation, and facilitating reliable data versioning and rollbacks.
What are the everyday work for Data Architect and Data Engineer?
Data Architect:
- Designing data architecture: This involves creating data models, defining data flows, and designing data storage solutions that meet the organization's requirements.
- Data governance: Implementing and enforcing data governance policies, ensuring data quality, security, and compliance with regulations.
- Collaborating with stakeholders: Working closely with business stakeholders, data engineers, data scientists, and analysts to understand their requirements and align data solutions with business objectives.
- Technology evaluation: Assessing new technologies, tools, and frameworks for their suitability in the data architecture stack.
- Performance tuning: Optimizing database performance, query tuning, and ensuring scalability of data systems.
- Documentation: Creating and maintaining documentation for data architecture, data dictionaries, and data lineage.
Data Engineer:
- Data pipeline development: Building and maintaining data pipelines to ingest, transform, and load data from various sources into data storage systems.
- Data integration: Integrating data from disparate sources and formats, ensuring data consistency and integrity.
- ETL/ELT processes: Developing and optimizing ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes to prepare data for analysis and reporting.
- Data warehouse management: Managing data warehouses, data lakes, or other storage systems, including schema design, partitioning, and optimization.
- Data quality management: Implementing data quality checks, monitoring data pipelines for anomalies, and ensuring the accuracy and reliability of data.
- Automation: Automating repetitive tasks, scheduling data jobs, and implementing monitoring and alerting systems for data pipelines.
- Performance optimization: Optimizing data processing and query performance, tuning database configurations, and improving overall system efficiency.
- Collaboration: Collaborating with data scientists, analysts, and business stakeholders to understand data requirements and deliver actionable insights.
