What is Data Engineering? | How to Become a Data Engineer | Data Engineering Certification | Edureka

Key Takeaways

[Music] foreign [Music] engaging story about two friends Alex and Bob but before we go ahead if you haven't already please subscribe to our edureka YouTube channel and click the Bell icon to never miss out any updates from us also if you are looking for our data ensuring Masters program from edureka do check out the link given in the description below so now let's get started with the story hey have I told you about the inflatable data engineering journey of a multinational e-commerce company no I don't think so what happened Alex well let me share this story with you Bob this company has lots of valuable data but struggle with scattered systems and outdated databases oh that's a tough situation what did they do about it Alex they embarked on a data engineering initiative Bob it was quite a journey can imagine so what changes did they make Alex they integrated their data into a centralized system and automated data cleaning and transformation it made a huge difference Bob that sounds so promising did it impact their operation selects definitely Bob they gained faster access to accurate data and generated real-time analytical reports impressive with the edges data governance as well Alex yes the implemented data governance practices for data quality privacy and compliance that's commentable it's a great example of how data engineering can transform a business what an inspiring story Alex yeah I thought you would find it fascinating Bob data Instinct truly has the power to drive success and Innovation together yeah thanks for sharing the story Alex it reinforces the importance of data ensuring in today's data driven world hello everyone this is Sanya from edureka and in this video we will be diving into the fascinating world of data engineering so data engineering is all about harnessing the power of data to drive meaningful insights and today's rapidly evolving digital landscape organizations are grappling with massive amounts of data and that's where data ensuring comes into play now let's take a quick look at the agenda for this video we will start by the introduction of what data ensuring else and why it is important for so many businesses outside then we will delve into the key components of data engineering such as data ingestion data integration data transformation and data storage next we will discuss some of the responsibilities of data engineers and the comparison between data engineer data analysts and data scientist after that we will delve into the installation process of popular tools and Technologies which is used in data engineering finally we will wrap up with a glimpse of what data pipelines are so let's understand what is data engineering First Data engineering involves the design development and maintenance of system and infrastructure to handle large volumes of data effectively it focuses on the extraction transformation loading and storage of data which ensure its quality and scalability of analysis now let's understand what are the key components of data engineering data engineering helps to collect data from the disparate sources and integrate into a unified format which allows organization to have a comprehensive view of their data it also involves processing and transforming data into a suitable format which ensures data quality consistency and applications it includes the development of data pipelines and processing system that enable the efficient processing and Analysis of data this includes batch processing for large-scale data transformation and real-time processing for immediate insights and actions data engineering plays a crucial role in enabling data-driven World by providing clean integrated and accessible data data ensuring empowers organizations to make a informed decision based on accurate insights and Analysis overall it is used to handle the complexity of data processing storage and integration which ensures that the organization can leverage the full potential of their data assets for strategic operations and Innovations now after the thorough understanding of what data engineering is and its key features also we will delve into the importance of data engineering so here are some of the key reasons why data engineering should be imported or why data ensuring is important so our first reason is it focuses on building scalable and efficient data processing systems by optimizing data pipelines leveraging distributed computing Technologies and employing the performance tuning techniques data Engineers ensure that the organization can handle large volumes of data and Achieve faster processing times it provides the foundation for advanced analytics and machine learning initiatives by structuring and preparing data in a suitable format data Engineers enable data scientists and analysts to extract valuable insights build predictive models and develop machine learning algorithms data engineering enables organization to process and analyze data as it arrives this is crucial in scenarios such as fraud detection recommendation systems iot applications and monitoring systems that require immediate insights and actions as we already know the importance of data engineering we will cover the real world applications based on this so our first application is e-commerce sites so data engineering is used to collect and process large volumes of customer data transaction data and product data this enables e-commerce companies to predict recommendations optimize pricing strategies and improve inventory management our second example is social media sites like data engineering plays a crucial role in collecting processing and analyzing social media data which allows companies to monitor brand sentiment track user interactions and direct insights for targeted marketing campaigns so our third example is in finance and banking sector data ensuring is used to handle financial data including transaction records customer information and Market data it enables fraud detection risk assessment algorithmic trading and personalized Financial Services also okay so now the food application is in the healthcare sector data insurers employed to manage and analyze patient records Medical Imaging data and clinical trial data it enables Healthcare Providers and researchers to gain insights improve patient outcomes and develop predictive models so this example is highlight the diverse application and the importance of data Engineering in various Industries demonstrating how it enables organization to Leverage The Power of data for operation and Innovation now the data ensuring process involves several key steps including data injection transformation storage processing and integration let's discuss each step in more detail so our first step is data ingestion data ingestion refers to the process of collecting and importing data from various sources into a data system or data pipeline this can involve extracting data from databases files apis streaming platforms or the other resources the goal is to gather relevant data and make it available for the further processing and Analysis Second Step would be the data transformation once the data is ingested it often needs to be transformed into a suitable format for analysis or storage this data transformation involves cleaning validating and restructuring the data to ensure consistency and usability this step may include tasks such as data filtering aggregation normalization data type conversion or the application of business rules so our third step would be data storage so after the data is transformed it needs to be stored in a structured manner right so this typically involves using databases or data storage system they provide efficient storage and retrieval capabilities popular choices for data storage include relational databases data warehouses data lakes or distributed file systems the selection depends on the specific requirements of the project such as data volume assist patterns and the analytical records so our next step would be data processing data processing involves performing computations and analysis on the stored data this step can include tasks such as data aggregation data Improvement data summarization statistical calculation or machine learning algorithms data processing can be done through various tools such as the SQL queries data processing engines or the custom scripts Next Step would be the data integration so in the step data integration involves combining data from multiple sources to create a unified and comprehensive view the step is crucial when dealing with heterogeneous data sources or Wing different style produced data this needs to be Consolidated data integration can be achieved through data consolidation or by using extract transform load process to combine and merge data from various sources next our last step would be the data governance so data governance is nothing but a framework and a set of process which ensures the effective management and utilization of data assets within an organization it involves establishing policies procedures and guidelines for data management data quality and data usage all the steps in data Engineering Process are typically iterative and may require continuous monitoring optimization and maintenance to ensure data quality reliability and performance data Engineers play a crucial role in designing and implementing efficient and scalable data pipelines to support data-driven applications and Analytics Now we move ahead with the key responsibilities of data Engineers which typically includes designing and developing scalable and efficient data pipelines which extract transform and load data from various sources to our targeted systems next would be the managing and optimizing data storage infrastructure for performance scalability and reliability selecting and configuring appropriate data storage technology such as relational databases data warehouses or nosql databases to meet data storage and retrieval requirements data Engineers are also responsible for applying data transformation techniques to clean filter Aggregate and structure data for analysis or consumption by Downstream systems they are also responsible for implementing data processing tasks using programming languages or data processing Frameworks to manipulate and transform data efficiently now after analyzing the key responsibilities of data ensuring we will proceed with our next topic can understand the key differences between these three roles that is data engineer data analyst and data scientist this we are the distinct role within the field of data science each with its own set of responsibilities and skill requirements so our first profile is of data engineer so data engineer are responsible for Designing building and maintaining the infrastructure and system that enable data storage processing and retrieval as we all know they focus on creating and managing the data pipelines and architecture necessary for efficient data collection transformation and Storage data Engineers also work closely with software engineers and database administrator to ensure data is accessible reliable and scalable they typically work with tools like Hadoop spark SQL ETL Frameworks and cloud-based platforms for data processing and Storage then our next profile would be the data analyst so data analysts are focused on analyzing and interpreting data to derive meaningful insights they work with structured and unstructured data to identify patterns strengths and correlations data analysts are proficient in statistical analysis data visualization tools and data quarant techniques as well they often use tools like SQL Excel Tableau or power bi to analyze data and create reports and dashboards after that our next profile would be the data scientist data scientist process a blend of skills from mathematics statistics programming and domain knowledge they leverage their expertise to develop and Implement complex algorithms and models to solve integrate data patterns or extract insights from large data sets data scientists employ techniques like machine learning predictive modeling and statistical analysis to build predictive models uncover patterns and make predictions also they also collaborate with stakeholders to Define business problems and design experiments to gather their data so to summarize this data Engineers focus on the infrastructure and data pipelines data analysts work on analyzing and Reporting data while data scientists concentrate on Advanced modeling and extracting insights so to make all this happen data Engineers rely on powerful tools and Technologies platforms like a party spark Apache Kafka Sequel and nosql databases and cloud services such as AWS and gcp provides the building blocks for efficient data engineering this tools help process vast amounts of data facilitate real-time data streaming and ensure secure and scalable data storage now let's understand what are the data pipelines are so here is the glimpse of what data pipelines are and why we use data pipelines so data pipelines are the series of steps that extract transform and load data from source to destination itself okay so they enable the efficient and automated flow of data through different stages ensuring data quality consistency and availability let's understand each step one by one so the first step is extraction the extraction phase of ETL involves retrieving data from various sources such as databases files apis or streaming platforms the goal is to extract the relevant data needed for further processing and Analysis this process typically includes establishing connection to the data sources performing data queries or using data extension tools to pull the required data into the data pipeline Next Step would be the transformation the transformation phase of ETL focuses on cleaning validating and reshaping the expected data to ensure its quality and consistency this step involves applying various operations and rules to the data such as data filtering data type conversions data aggregation data enrichment or data normalization the transformation process aims to make the data suitable for analysis storage or integration into the destination system Next Step would be the loading the loading phase of ETL involves storing the transform data into the targeted system such as databases data warehouses or data Lakes then the data is loaded in a structured format it aligns with the schema of format of the destination system this phase may include tasks such as data mapping schema matching data partioning or indexing to optimize data storage and retrieval after that our next step would be the or maybe we call as our last step would be the monitoring and handling monitoring and handling referred to the ongoing monitoring management and the mandarins of data Pipelines okay so now let's discuss about batch processing and real-time streaming Pipelines so in a batch processing pipeline there is a delay between time data is collected and when it is processed this delay can range from minutes to hours or even days depending on the scheduled intervals on the other hand real-time streaming pipeline aim to process data as it arrives which results in a minimal latency data is processed analyzed in near real time or within a very low delay the second point is batch processing pipelines are designed to handle large volumes of data efficiently they can process and analyze massive amounts of historical data in a batched mode on the other hand real-time streaming pipelines focus on processing data as it arrives making them more suitable for handling data streams with continuous High Velocity data updates now the third point is batch processing pipelines typically utilize batch processing Frameworks like Apache spark or Hadoop Map Reviews this Frameworks process data in chunks or batches which allows for parallel processing and optimized resource utilizations also while real-time streaming pipelines often use streaming Frameworks like Apache Kafka Apache Flink or Apache storm this Frameworks enable continuous processing of data extremes supporting low latency operations and real-time Analytics now the fourth point is batch processing pipelines are commonly used for tasks that involve historical analysis generating periodic reports or data preparation for machine learning models they are well suited for scenarios where processing time is not critical but analyzing large volumes of data is essential on the other hand real-time streaming pipelines are ideal for application that require real-time monitoring immediate response or instant insights based on live data use cases include like fraud detection real-time recommendation systems network monitoring or iot sensor data analysis so our last point is batch processing pipelines often requires significant Computing resources during the processing phase as zip process large volumes of data in a batch mode where is real-time streaming pipelines also requires Computing resources but are more focused on low latency processing and continuous data streams requiring efficient resources allocation and management so it's important to note that there can be the overlap between batch processing and real-time streaming pipelines and hybrid architectures combining both approaches are common so that wraps up our Deep dive into the world of data engineering we have covered the essential aspects of data pipelines governance and security giving you a comprehensive understanding of how data is ingested transform stored and processed we hope this video has provided you with valuable insights and sparked your curiosity to explore further if you found this content informative and engaging be sure to subscribe to our Channel hit the notification Bell and join our community for captivating this question on data engineering and other fascinating topics thank you I hope you have enjoyed listening to 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