How do you perform common data engineering tasks?

The famous 'E' of ETL/ELT. The task of procuring, gathering, and obtaining data from various sources, such as databases, APIs, files, web pages, or streams, is known as data extraction. This can be done via pull or push strategy too. Data extraction can be accomplished by a variety of approaches, including SQL queries, Python scripts, web scraping, and enterprise tools like Informatica, Datastage etc. The purpose of data extraction is to collect raw data that may be processed and modified for further analysis or modeling. Handling multiple data formats, kinds, and structures, dealing with errors and exceptions, re-runs, integrity, change in structure and guaranteeing data security and privacy are some of the issues of data extraction.

In the world of data engineering, data extraction is like scanning a product's barcode in supermarket It begins with identifying and selecting specific data, just like scanning a barcode. Our data source, much like the supermarket's database, stores the information we want to extract Initiating the process is akin to starting the scan, whether through web scraping, database queries, or APIs. Just as the supermarket scanner deciphers barcodes, data extraction processes and structures raw data The scanned product information is temporarily stored on the screen, just as extracted data is saved for analysis In both scenarios, data extraction is vital for transforming raw data into usable format enabling analysis and informed decision-making

In my experience challenges of data extractions are speed or format of collection , sanity of data and reconciliation with source. Current world is moving for data to be consumed as soon as it is generated so event and streaming based scaling architecture is important. Additionally making sure proper data checks are in place is important so that downstream does not waste resources to process incomplete or wrong data. Reconciliation or handshake with source systems is also important to confirm if data is complete and there are no leakages.

In the digital age, data extraction stands as the critical first step towards insightful analytics and strategic decision-making. It’s the process of retrieving data from varied sources like databases, APIs, and web pages, setting the foundation for advanced analysis and modeling. Challenges to Navigate: ? Managing diverse data formats and structures. ? Ensuring accuracy amidst errors and inconsistencies. ? Upholding data security and privacy in compliance with regulations.

E- Engineering and E-Extraction, Data extraction is an important task of data engineering process, to precisely gather data from various sources be it static data or live streaming data and use it for further processing. Few tips to handle data extraction includes- * Using the right tools and technologies * Have a clear understanding of your data sources * Test your extraction processes thoroughly * Monitor your extraction processes Despite the challenges, data extraction is an essential task for many businesses and organizations.

Data transformation is very broad, however in context of ETL these are some of the widely performed tasks like Data enrichment, cleansing, filtering, aggregation. Let me give an example of Data Enrichment, deduplication, derivation etc. for example sometimes we have only 5 digit zipcode in data but we have to get the additional 4 digit so we join with an external dataset and "transform" the zipcode data. Similarly, many a times we need the "AGE" column in a table updated daily, so we derive it from the birthdate.

Transforming data is essential for uncovering valuable insights. By focusing on data quality and organization, we not only make analytics smoother but also provide decision-makers with trustworthy data. It’s important to find the right mix of comprehensive data handling and fast performance to facilitate immediate decision-making.

Data transformation is a crucial step in data engineering. Data engineers must validate data for junk, null, and duplicate values to ensure that the analytics team receives high-quality data. There are many different tools that can be used for data transformation, including: 1) Databricks with PySpark 2) Synapse Analytics with PySpark 3) Dedicated SQL Pool with SQL stored procedures 4) ADF Dataflow with Dedicated SQL Pool Databricks and Synapse Analytics are both cloud-based data platforms that offer PySpark, a powerful distributed computing framework for data processing and analysis. Dedicated SQL Pool is a massively parallel processing (MPP) database engine that is part of Azure Synapse Analytics.

Data transformation is the refining process in data engineering, where raw data is shaped, enriched, cleansed, and augmented to enhance its quality and usability, like turning unprocessed materials into valuable products This process has several key tasks: Data Enrichment: Imagine having a customer database with email addresses but want to personalize marketing efforts by including location data. We can "enrich" the dataset by linking the email addresses with geolocation data, enabling targeted regional campaigns. Deduplication: In a sales order system, we may encounter multiple entries for the same customer. Deduplication would involve identifying & removing the duplicate customer records to maintain a clean and accurate sales DB.

In the domain of data engineering, mastering the art of data transformation is essential. In my journey, I've realized the pivotal role of meticulous data cleaning and validation to pave the way for enriched analysis later on. Utilizing versatile tools like Pandas or Spark can empower engineers to perform a range of operations seamlessly. I always emphasize a proactive approach to data quality issues — setting up regular audits and maintaining a comprehensive error log have often been game-changers in my projects. Let's not overlook the significance of optimizing for performance; a well-structured data transformation process can significantly reduce computational costs and save resources.

Data loading is crucial for populating data storage systems, enabling analysis, reporting, decision-making, and making accurate and timely information accessible for businesses and organizations The initial data warehouse load consists of filling in tables in the schema data warehouse and then checking whether it is ready to use Steps to load data warehouse fact tables: Create temp table Populate temp table Update existing records Insert new records Perform error handling and logging When loaded data approaches a fixed-period extraction process, or when we are using reverse cancellations, data deletion might also be an excellent option to consider. Loading the data to the fact tables sometimes might also require deduplication of records

Choosing a data loading strategy while loading into datawarehous or lakehouse in cloud. Data size and volume: If you are loading a large volume of data, choose a scalable and load-tolerant strategy. Performance: If you need to load data quickly, choose a high-performance strategy. Budget: Some data loading strategies, such as Databricks and snowflake, can be more expensive than others, So use small files and parallel processing to reduce the time. Choose partitioning/clustering columns: Partition by WHERE clauses. This improves performance by letting Snowflake skip irrelevant partitions. Cluster by JOIN clauses. This improves performance by keeping related data together.

I have engineered a bespoke Python utility that facilitates automated data profiling, serving as a preliminary checkpoint before data loading. This tool capitalizes on the Dash framework to offer users an interactive visualization of the profiled dataset.

Data loading plays a pivotal role in ensuring data is accessible and usable. Methods like batch, incremental, and streaming loading cater to different data management needs. Employing techniques like partitioning, compression, and encryption enhances data efficiency and security. The objective is to facilitate fast and reliable data access for analysis or modeling. Key challenges include managing diverse data schemas and formats, maintaining data integrity and consistency, and optimizing the balance between storage costs and performance. Addressing these challenges effectively is crucial for maintaining a robust data infrastructure.

In my practical experience, data loading is essentially the cornerstone of any robust data ecosystem. Often, I found myself deeply engrossed in strategizing the optimal data loading methodology; sometimes it was batch loading where we dealt with substantial data volumes and planned uploads during low-traffic hours to ensure efficiency. At other times, the focus shifted to streaming loads to facilitate real-time analytics and reporting, which was vital for departments that required up-to-the-minute data.

One common method of data modeling in data engineering is Dimensional modeling. It is commonly used in data warehousing. It focuses on creating data models optimized for querying and reporting. In this method, data is organized into two main types of tables: facts (which contains numerical measures) and dimensions (which contains descriptive attributes). This approach simplifies complex data into a star or snowflake schema, making it easier for analysts to perform multidimensional queries and generate reports.

Data modeling makes it easier for developers and other stakeholders to view and understand the relationships between data in a database or data warehouse. A good data model can have the following benefits: Reduce errors in software and database development. Increase consistency in documentation and system design across the enterprise. Improve application and database performance. Ease data mapping throughout the organization. Improve communication between developers and business intelligence teams. Example: Data modeling in retail enhances inventory management by analyzing sales and external factors. Amazon's fulfillment centers exemplify this, using data modeling to streamline their supply chain for efficient deliveries.

Hugely under looked part of data engineering! If you are working on a large dimensional DB with many layers, it becomes extremely challenging to work without a data model. Taking the time to sit down and build one will usually pay off long term and make your life a lot easier.

I really enjoy data modeling - designing logical and efficient data pipelines and architectures. Determining the optimal flow of data from ingestion to storage to analysis excites me. For example, building a scalable pipeline to move IoT sensor data from Kafka to a cloud data warehouse leverages my analytical strengths. On the flipside, I sometimes struggle with more mundane tasks like ETL scripting and debugging. The work is important but less intellectually stimulating for me. But I know mastering the fundamentals is key to being an effective data engineer, so I embrace the challenge! My goal is to become a well-rounded engineer capable of tackling the full spectrum of responsibilities.

As a data engineer, modelling is deep waters that I have been afraid to swim on. However after a slight stint in the same, I have come to appreciate its importance. Data models are the building blocks of any Data warehouse. A small mistake in choosing the primary key can cause the whole setup to come tumbling down.

As we strive to build robust, error-free data pipelines and SQL data models, the value of unit testing in data engineering becomes increasingly clear. Unit testing is the practice of testing individual units or components of software to ascertain that each performs as expected. These could be anything from functions in a Python script to individual SQL queries. By validating each unit’s correctness, we significantly increase the overall reliability of our software, or in this case, our data pipelines and data models. The adoption of unit testing ensures that our data, as well as our business logic, are accurate. This leads to high-quality data that our data analysts, scientists, and decision-makers can trust.

Data quality testing becomes more interesting when you consider big data where the volume of the data being tested is vast. You will really want to do testing with tooling that also makes use of a distributed architecture. A library like Deequ, built on Apache Spark, can be integrated right into data pipelines to assert that the output meets business requirements. Similar tooling includes Great Expectations and platform offerings like Delta Live tables on Databricks.

Importance of Data Testing: Data Quality Assurance: Data testing ensures that the data is free from errors, inconsistencies, and inaccuracies, which is essential for informed decision-making. Pipeline Reliability: By validating data at each stage of the pipeline, data testing helps identify and rectify issues early, preventing data corruption downstream. Compliance: In regulated industries, data testing is crucial for compliance with data governance and privacy regulations, such as GDPR or HIPAA.

I have devised QA frameworks to automate data testing & cleansing, including a SQL-based framework as well as one utilizing Python and Power BI. These advanced frameworks have the versatility to interface with a variety of sources such as Snowflake, SQL, Oracle, and Flatfiles to adeptly reconcile data from the source to the destination. Moreover, these efficiently execute data-cleaning processes.

Unit testing for repository code is usually the first step of a robust CI/CD pipeline, since these tests can ensure that the code supporting ETL pipelines or applications will not produce many types of common anomalies or unexpected behaviors. Things like GitHub actions for example can even do test runs on code to see if the outputs are within an expectable range of parameters. In GCP, Dataflow Can be used to run test pipelines, especially if you are working with streaming data. And in BigQuery, SQL queries can be developed for assessing the quality of data in the warehouse.

A data team must always plan well in advance to monitor the data pipelines and systems for ensuring that the data is reliable, available, and accessible to users. Few important aspects to consider that can be monitored includes - Data pipeline performance Data quality System health Security and compliance To adapt to such monitoring activities following tools can be used - Cloud monitoring tools: Cloud monitoring tools such as AWS CloudWatch, Azure Monitor, and GCP Monitoring. Open source monitoring tools: There are a number of open source monitoring tools available, such as Prometheus and Grafana. Also, it is important to have a recovery plan in place for any problems that are identified.

Data Monitoring for Business Growth: Data monitoring involves the continuous tracking, analysis, and management of data to ensure its quality, reliability, and security. Here's how data monitoring contributes to business growth: Early Issue Detection: Data monitoring systems are designed to detect anomalies and discrepancies as soon as they occur. By identifying issues early, businesses can take immediate corrective actions, preventing data-related problems Improved Decision-Making: High-quality, reliable data is essential for informed decision-making Enhanced Customer Experience: Businesses that rely on customer data can use data monitoring to ensure that customer profiles, preferences, and transactions are accurate

There should always be a Disaster Recovery (DR) plan, with a prompt RTO and RPO identified upfront, for any On-prem, Cloud or hybrid approach. This ensures business is not impacted.

I possess expertise in the art of designing and developing diverse monitoring solutions for ETL monitoring. Furthermore, I am proficient in developing several frameworks on SQL, specifically, for SSIS monitoring purposes. My proficiency also extends to monitoring Azure pipelines (ADF, Databricks, Synapse), which is accomplished by creating frameworks with the use of SQL, ADF, and Logic App. Additionally, I have demonstrated my technical competency by designing and implementing a native Power BI application, which is an extension of Azure Monitor and Azure Log Analytics.

Data Engineering relies on robust monitoring techniques such as logging, auditing and alerting. Tools like Grafana, Azure Monitor, Azure Log Analytics and AWS CloudWatch aid in this process to track potential issues including tracking pipelines, metrics and data quality. Few key challenges: 1. Integrate data from different sources with different formats complicates the monitoring efforts. 2. Real-time data monitoring requires efficient algorithms and systems to handle continuous streams. 3. Data Security to safeguard the sensitive data during monitoring is crucial and continuous challenge. Addressing these challenges involves a combination of advanced technologies, data engineers and refinement of monitoring strategy practices.

Data engineering activities can be broadly classified into the following three stages: - Data ingestion: To extract data from source systems, then clean, transform, and load it into a target system. It can involve ETL/ELT processes with batch or streaming data to load into data warehouse or data lakes. - Data processing: Applying operations on data, such as aggregations, joins, and filtering. Using SQL or Spark is one of the preferred options to process the data. - Data delivery: To make the data available to users in a timely and efficient manner. Creating dashboards, reports or building API to give access to end user can give appropriate data to end users.

In addition to the above-discussed data processes, Data Integration Performance optimization Data governance and Automation are also one of the important elements to be considered for building a versatile solution

Containerization is key for us. In every step of the ETL process (and even for data testing) we make our life easier by using our own developed generic containers for each task in the pipeline. This way we shorten development time along with some other benefits.

However you perform the tasks, remember one thing: AUTOMATE! If your tasks are not automated in a production environment, you are definitely not doing it right.

In addition to the core Data Engineering processes, Data Profiling is also essential. This involves analyzing data to understand its structure and quality. Profiling tools like Azure Purview, Data Catalog, AWS Glue help assess data completeness, accuracy and guarantees a strong foundation for subsequent processes. Data Profiling enhances overall data integrity and supports in making informed decisions.