ArunKumar R’s Post

🎯Delta Lake: =========== Before we dig deep into delta lake first lets discuss about two terms: 1. Data warehouse 2. Data lake A data lake is a central location that holds a large amount of data in its native,raw format. Compared to hierarchical data warehouse which stores data in files or folders a data lake uses a flat architecture and object storage to store the data. Object storage stores data with metadata tags and a unique identifier which makes it easier to locate and retrieve data across regions and improve performance. By leveraging inexpensive object storage and open formats data lakes enables many applications to take advantage of the data. Basically data lake were developed in response to the limitations of data warehouses. when the data increases and the patterns of the data increases it become quite expensive and troublesome to handle such amount of data. data lake are used to consolidate all of an organizations data in a single,central location where it can be saved without the need to impose a schema(formal structure for how the data is organized) Data in all stages of the refinement process can be stored in a data lake. Unlike data warehouses data lakes can process all data types: unstructured and semi-structured data like images,video,audio etc... ** Datawarehouse only support structured data. Now there were some challenges with data lake : 1. Data lake cannot support DML operations. 2. Data lake stores the data in a raw format so it is very difficult to perform some operations on top of it. 3. While doing operations if get failed or stuck it will leave the system in that state while in case of datawarehouses it will roll back. 4. As the size of the data in a data lake increases the performance gets slow as the bottle necks arises metadata management,improper data partitioning. 5. Data lakes are hard to properly secure and govern due to lack of visibility and ability to delete or update data. Image credit : Google Images #spark #databricks #dataengineer #data

DATA WAREHOUSE VS. DATA LAKE 1) One of the key differences between data warehouse and data lake architectures is how they store data. A data warehouse stores data in a structured and normalized way, using relational databases or columnar formats. This reduces data redundancy and improves data quality, but also requires more processing and storage resources. A data lake stores data in a flat and flexible way, using object storage or file systems. This enables data scalability and diversity, but also increases data complexity and governance challenges. 2) A data warehouse processes data before loading it into the repository, using ETL tools and pipelines. This ensures that the data is clean, consistent, and ready for analysis, but also limits the scope and speed of data ingestion. A data lake processes data after loading it into the repository, using various tools and frameworks, such as Hadoop, Spark, or SQL. This enables faster and more diverse data ingestion, but also requires more skills and resources to analyze the data. 3) A third key difference between data warehouse and data lake architectures is how they support different use cases. A data warehouse is best suited for use cases that require structured and standardized data for reporting and analysis, such as dashboards, KPIs, or OLAP cubes. A data warehouse can answer predefined and repeatable questions, such as "What is the monthly revenue by region?" or "How many customers bought product X in the last quarter?". A data lake is best suited for use cases that require raw and unstructured data for exploration and discovery, such as machine learning, natural language processing, or sentiment analysis. A data lake can answer ad-hoc and complex questions, such as "What are the main topics of customer reviews?" or "How can we predict customer churn based on behavior patterns?". 4) The final key difference between data warehouse and data lake architectures is the trade-offs that they involve. A data warehouse offers advantages such as data quality, consistency, and reliability, but also disadvantages such as data rigidity, latency, and cost. A data lake offers advantages such as data flexibility, scalability, and diversity, but also disadvantages such as data complexity, governance, and security. Therefore, choosing between a data warehouse and a data lake depends on your business needs, goals, and resources, as well as the characteristics and requirements of your data. #data #datawarehouse #dataengineer #analytics #solutions #linkedin #analyst

📢 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠? ✨ Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. 🎯 𝐆𝐨𝐚𝐥? The goal of data modeling to illustrate the types of #data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes. 📎 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐬: > Conceptual data models > Logical data models > Physical data models 📜 𝐃𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬: 1- Identify the entities. 2- Identify key properties of each entity. 3- Identify relationships among entities. 4- Map attributes to entities completely. 5- Assign keys as needed, and decide the degree of #normalization. 6- Finalize and validate the data model. 🖇 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠: - Hierarchical data models - Relational data models - Entity-relationship (ER) data models - Object-oriented data models - Dimensional data models (#Star and #Snowflake) In summary, #DataModeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand relationships among the data in a database or #datawarehouse. #DataEngineering #DataScience #Microsoft #Azure #Model #OOP

📘 Delta Lake: =========== Before we dig deep into delta lake first lets discuss about two terms: 1. Data warehouse 2. Data lake A data lake is a central location that holds a large amount of data in its native,raw format. Compared to hierarchical data warehouse which stores data in files or folders a data lake uses a flat architecture and object storage to store the data. Object storage stores data with metadata tags and a unique identifier which makes it easier to locate and retrieve data across regions and improve performance. By leveraging inexpensive object storage and open formats data lakes enables many applications to take advantage of the data. Basically data lake were developed in response to the limitations of data warehouses. when the data increases and the patterns of the data increases it become quite expensive and troublesome to handle such amount of data. data lake are used to consolidate all of an organizations data in a single,central location where it can be saved without the need to impose a schema(formal structure for how the data is organized) Data in all stages of the refinement process can be stored in a data lake. Unlike data warehouses data lakes can process all data types: unstructured and semi-structured data like images,video,audio etc... ** Datawarehouse only support structured data. Now there were some challenges with data lake : 1. Data lake cannot support DML operations. 2. Data lake stores the data in a raw format so it is very difficut to perform some operations on top of it. 3. While doing opeartions if get failed or stuck it will leave the system in that state while in case of datawarehouses it will roll back. 4. As the size of the data in a data lake increases the performance gets slow as the bottle necks arises metadata management,improper data partitioning. 5. Data lakes are hard to properly secure and govern due to lack of visibility and ability to delete or update data. #learnsparkbyaman

First Stage of Data Warehousing: 🔍 Data Extraction, Transformation & Loading (ETL): The Power Behind Smart Decisions 💡 In today’s data-driven world, organizations rely heavily on ETL (Extract, Transform, Load) processes to manage large amounts of data. Whether you’re new to ETL or a seasoned pro, let’s break down this process in simple steps: 1) Extract: Get the Data The first step in ETL is extracting data from various sources like databases, cloud services, or files. Think of it as gathering ingredients before you start cooking! 🍎📊 Sources can include: Databases (SQL, NoSQL) APIs Excel/CSV files Goal: Pull data without altering it from its original source. 2) Transform: Clean & Organize the Data Next, it’s time to transform the raw data into a usable format. 🛠️ This is like prepping your ingredients—chopping, slicing, and seasoning! 🥗 In this stage, data is cleaned, filtered, and modified: Remove duplicates Standardize formats (e.g., dates, currencies) Enrich with additional data Apply business rules Goal: Make the data ready for analysis by ensuring it’s accurate, consistent, and usable. 3) Load: Put the Data to Work Finally, load the transformed data into a database or data warehouse where it can be analyzed. 🚀 You’re serving the meal now, making it available for reporting, visualization, or machine learning! 📈 This could involve loading: A data warehouse (like Snowflake or Redshift) Cloud storage solutions Business intelligence tools (like Tableau or Power BI) Goal: Store the data where it can be accessed efficiently for business decision-making. ETL processes enable companies to leverage vast amounts of data and turn it into meaningful insights. Whether you’re making strategic decisions or diving into analytics, ETL is the engine that drives data-driven success. Key Takeaways: Extract data from various sources. Transform it into a clean, usable format. Load it into a system for analysis. Mastering ETL can transform your data processes and help your business thrive! 🌟 #ETL #DataScience #BigData #DataAnalytics #TechTips #BusinessIntelligence #DataTransformation #DataDriven #LinkedInLearning

Learn and practice the below points to be a better Databricks engineer. It's important that you know how to optimize your slowly running Databricks code. Optimize Data Layout and File Size Use Delta Lake's auto-tuning capabilities to manage file sizes automatically. Regularly run the OPTIMIZE command with Z-ordering for better data layout. Implementation: Set up a schedule to run OPTIMIZE jobs on a separate cluster to avoid impacting job SLAs. Control Data Shuffling Use broadcast hash joins to reduce data shuffling, especially for smaller tables. Adjust the broadcast join threshold settings (spark.sql.autoBroadcastJoinThreshold). Implementation: Explicitly use broadcast hints in SQL queries for small tables. Remediate Data Skewness Identify skewed data using Spark UI and metrics. Apply skew hints or use the AQE feature to handle skewed partitions. Implementation: Regularly monitor job performance and adjust configurations as needed. Prevent Data Explosion Be cautious with the explode() function and joins that can increase data volume. Reduce input partition sizes or increase shuffle partitions to manage data size. Implementation: Analyze data flow and adjust partitioning strategies accordingly. Enhance Data Skipping and Pruning Enable Delta data skipping by configuring the number of indexed columns. Use column pruning and predicate pushdown to minimize data read. Implementation: Review queries to ensure only necessary columns are selected and filters are applied early. Utilize Data Caching Prefer Delta cache over Spark cache for better performance. Use caching for intermediate results that are accessed multiple times. Implementation: Configure clusters to use Delta cache and identify key datasets for caching. Optimize Delta Merge Operations Use partition and file pruning to reduce the amount of data processed during merges. Enable low shuffle merge to maintain data organization. Implementation: Regularly review and optimize merge strategies based on data size and update frequency. Regular Data Purging Schedule the VACUUM command to remove stale data files. Adjust retention settings to balance between data availability and performance. Implementation: Automate VACUUM execution as part of maintenance workflows. Leverage Delta Live Tables (DLT) Use DLT for building and managing data pipelines with automatic data quality checks. Implement Enhanced Autoscaling for cost-effective streaming workloads. Implementation: Transition existing ETL pipelines to DLT for improved reliability and monitoring. Optimize Cluster Configuration and Usage Choose appropriate instance types based on workload characteristics. Enable autoscaling for interactive and development clusters. Implementation: Regularly review cluster configurations and adjust based on workload demands and SLAs. #creditgoestotheowner

What is data lake ?? A data lake is a centralized repository that allows you to store all types of structured, semi-structured, and unstructured data at scale. Unlike traditional databases, which require predefined schemas, data lakes enable you to store raw data in its native format until it's needed for analysis. Then after searching and reading about this I had a thought that what is main difference between data warehouse and a data lake ?? Let's discuss it . 🚀 Data Warehouse vs. Data Lake 🌊 Understanding the key differences between these two data management solutions is crucial for an effective data strategy! 1. Structure: - Data Warehouse: Stores structured data with a predefined schema. - Data Lake: Accommodates structured, semi-structured, and unstructured data in raw format. 2. Purpose: - Data Warehouse: Optimized for analytics and reporting. - Data Lake: Ideal for big data analytics and machine learning. 3. Processing: - Data Warehouse: Follows ETL (Extract, Transform, Load). - Data Lake: Utilizes ELT (Extract, Load, Transform). 4. Cost: - Data Warehouse: Generally more expensive. - Data Lake: More cost-effective for large volumes of data. 5. Users: - Data Warehouse: Business analysts. - Data Lake: Data scientists and engineers. Understanding these differences can help you choose the right solution for your data needs! 💡 #DataAnalytics #DataManagement #BigData #learningandhelping #keenlearner #smallsteps

#journey_of_data In the journey of data, it's crucial for data seekers to grasp the distinctions between Data Warehouses, Data Marts, and Data Lakes. Data repositories such as data warehouses, data marts, and data lakes are vital for storing data used in reporting, analysis, and gaining insights. Each has its own distinct purpose, storing various types of data and enabling different access methods. Data Warehouses:   -Centralized hubs for unified data, housing both current and historical information from various sources. -Central repositories integrating data from multiple sources. -Store both current and historical data post-cleansing, conformance, and categorization. -Traditionally store relational data from transactional systems like CRM, ERP, HR, and Finance. - Embracing NoSQL tech for non-relational data. - Often structured with a three-tier architecture: database servers, OLAP server, and client front-end. - Cloud-based versions offer advantages such as reduced costs, scalable storage and compute power, and quicker disaster recovery. Data Marts: - it's more like subsections of data warehouses designed for specific business functions or user groups. - Varieties encompass dependent, independent, and hybrid data marts. - Dependent data marts offer focused analytics with isolated security and performance. - Independent data marts originate from sources beyond enterprise data warehouses. - Hybrid data marts amalgamate inputs from diverse sources..  Data Lakes: - Storehouses for vast amounts of structured, semi-structured, and unstructured data in native formats. - No requirement to define structure or schema prior to data loading. - Governed repositories enabling agile data exploration for analysts and data scientists. - Deployment options include cloud object storage, distributed systems like Apache Hadoop, or relational database management systems. - Provide advantages such as storing diverse data types, scalability, agility, and accommodating various use cases. #DataWarehousing #DataMarts #DataLakes #DataManagement #PowerBI #DataAnalytics #Microsoft #DataInsights #BigData #DataAnalytics #DataScience #DataManagement #DataEngineering #BigData  #Week_in_Data #NOSQL  #DataManagement #BigData #DataInfrastructure  #Day_in_Data

🚀 What is a Data Lakehouse? The Future of Unified Data Management 🚀 In today’s data-driven world, organizations need a system that provides the scalability of data lakes with the performance and reliability of data warehouses. That’s where the Data Lakehouse comes in! A Data Lakehouse combines the best of both worlds: • Scalable storage and processing like a data lake. • ACID transactions and schema enforcement like a data warehouse. 💡 Key Benefits of a Lakehouse on Databricks: • Real-time data processing: Process streaming data for immediate analysis. • Unified governance: Manage access, track data lineage, and secure sensitive data with tools like Unity Catalog. • Delta Lake: An optimized storage layer to maintain data consistency and reliability. • Data for everyone: Enables machine learning, data science, and BI—all from a single source of truth. • Schema evolution: Adapt to business changes without disrupting your pipelines. With a lakehouse architecture, modern organizations can unify their data science, machine learning, and BI workloads in one platform, eliminating silos and ensuring data freshness. 🌐 https://lnkd.in/dGwEchEj Have you used a lakehouse architecture before? Share your experience or questions below! 💬👇 #DataLakehouse #BigData #AzureDatabricks #DeltaLake #DataEngineering #MachineLearning #BusinessIntelligence #CloudComputing #DataArchitecture