Techelligence, Inc

🌟 Exciting News! 🌟 As the Director of Innovation, I am thrilled to share with you the importance of business executives leading the way with a strong and ethical AI framework. 🤝 According to a recent article by Cesareo Contreras, it is crucial for business leaders to champion responsible AI practices. [1] Cansu Canca, the director of responsible AI practice at Northeastern University’s Institute for Experiential AI, emphasizes that without executive support, responsible AI practices will not be effective. This top-down approach applies to various industries, including financial services and retail. 🏦🛍️ To equip current and aspiring C-Suite executives with the necessary knowledge, Northeastern University recently held a "Responsible AI Executive Education" course. This course aimed to train executives in implementing responsible AI practices. 🎓 Steve Johnson, the CEO of Notable Systems Inc., also shared his own AI framework consisting of five rules. These rules stress the importance of not blindly trusting AI systems and continuously verifying their accuracy. Johnson also emphasizes the need for human judgment and guidance in AI systems. 🤖💡 Now, let's dive into the main story! 📚 Artificial Intelligence Best Practices provide an overview of AI's potential impact on various industries. It discusses different AI technologies, including machine learning, natural language processing, computer vision, and robotic process automation. These technologies have the power to improve efficiency and customer service, provide competitive advantages, and enhance decision-making. 💼🚀 Building an AI system involves several steps, such as data collection, data cleansing, model selection, training, testing, optimizing, and deployment. It is crucial to include AI strategy in organizational goals, develop a strong ethics framework, ensure high-quality data, and address security and privacy concerns. 📊🔒 To leverage AI effectively, businesses can explore various AI tools such as generative AI tools, customer service AI tools, and human resources AI tools. Creating artificial intelligence templates, including a corporate AI policy, an AI governance framework, and plans for monitoring AI performance, can also be beneficial. 🛠️📝 Now is the time for action! Let's embrace AI and lead the industry with innovation and intelligence. Assess your business's readiness for AI, develop a clear strategy, and start with small, manageable projects. Together, we can unlock the full potential of AI and drive sustainable growth and success. 🌟💪 #AI #ArtificialIntelligence #ResponsibleAI #BusinessLeadership #Innovation #Ethics #DigitalTransformation References: [1] Why it’s important for business executives to lead the way with a strong and ethical AI framework: https://lnkd.in/dHuAUUcG

SCD Type 2 is most commonly used in the industry, in this when an attribute value in a dimension changes, instead of updating the existing row, a new row is inserted with the updated value. This allows for the coexistence of multiple versions of the same dimension in the table, each with its own valid time period. The newly inserted rows are typically assigned a surrogate key and timestamps to track the time of validity. Thanks Kushal Sen Laskar for curating this document, which cover practical implementation of SCD 2 in Pyspark 𝑹𝒆𝒑𝒐𝒔𝒕 𝒊𝒇 𝒚𝒐𝒖 𝒇𝒊𝒏𝒅 𝒊𝒕 𝒖𝒔𝒆𝒇𝒖𝒍 𝑬𝒗𝒆𝒓𝒚𝒅𝒂𝒚, 𝑰 𝒍𝒆𝒂𝒓𝒏 𝒂𝒏𝒅 𝒔𝒉𝒂𝒓𝒆 𝒔𝒕𝒖𝒇𝒇 𝒂𝒃𝒐𝒖𝒕: 🌀 Data Engineering 🌀 Python/SQL 🌀 Databricks/Pyspark 🌀 Azure 𝑾𝒂𝒏𝒕𝒆𝒅 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆 𝒐𝒏 𝒂𝒏𝒚 𝒕𝒐𝒑𝒊𝒄𝒔, 𝒇𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dGDBXWRY 👉𝐅𝐨𝐥𝐥𝐨𝐰 Riya Khandelwal 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭.

🚀 Microsoft Purview - Databricks Unity Catalog. Exciting news in the world of data management! 🌐 Microsoft Purview has introduced lineage tracking for Azure Databricks Unity Catalog! 📊✨ This powerful feature enhances the ability to understand and visualize data flow, ensuring transparency and trust in data assets. 🔍 With Microsoft Purview - Databricks Unity Catalog lineage tracking, you can now: Trace data origins effortlessly 🔄 Monitor transformations in real-time ⏱️ Enhance compliance and governance measures 📜 Pairing Microsoft Purview and Databricks Unity Catalog, organizations can achieve a comprehensive view of their data landscape. 🏞️ Purview’s robust data governance capabilities combined with Unity Catalog’s lineage tracking empower teams to make informed decisions while maintaining data integrity. 💪 #Microsoft #Databricks #UnityCatalog #DataGovernance #MicrosoftPurview #DataLineage

T𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 Databricks has transformed the data landscape, offering a powerful, unified platform for data engineering, analytics, and machine learning. Here’s a closer look at what Databricks offers and how it supports data-driven teams. --- 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬? Databricks is a cloud-based platform designed to handle big data processing, analytics, and machine learning in a single, collaborative environment. Built on Apache Spark, Databricks enables teams to work on large datasets with high speed and efficiency, making it a go-to choice for companies like Netflix and Amazon. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬   - Unified Analytics Platform: Databricks integrates data engineering, data science, and business analytics, reducing workflow complexity.   - Apache Spark Core: Spark’s high-speed data processing powers Databricks, enabling fast and scalable data handling.   - Delta Lake: A storage layer that enhances data lakes with ACID transactions and schema enforcement, ensuring data reliability.   - MLflow: Built-in tools for machine learning lifecycle management, covering model tracking, deployment, and management. 𝐂𝐨𝐫𝐞 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬   - Clusters: Run Spark jobs for data processing and machine learning.   - Workspaces: Collaborative spaces for sharing code, data, and visualizations.   - Jobs and Workflows: Automate and schedule ETL processes and machine learning pipelines.   - Delta Lake: Adds data consistency, reliability, and efficient storage. 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠   - MLflow Integration: Manages the entire ML lifecycle, from experiment tracking to model deployment.   - Real-Time Streaming: Processes data in real time, ideal for applications like fraud detection.   - Auto Loader and Structured Streaming: Enables efficient, scalable data ingestion and real-time analysis. 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐒𝐐𝐋 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 With Databricks SQL, analysts can use SQL queries to access large datasets, create visualizations, and build dashboards. It integrates with BI tools like Power BI and Tableau, providing easy access to data insights for decision-making. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 Databricks ensures enterprise-grade security with data encryption, role-based access control (RBAC), and adherence to compliance standards like GDPR and HIPAA, making it a secure choice for handling sensitive data. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐚𝐧𝐝 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬   - ETL and Real-Time Analytics: Simplifies data ingestion and transformation, powering real-time applications.   - Machine Learning: Seamlessly train and deploy models at scale.   - Business Intelligence: Run queries and visualize insights with Databricks SQL. --- ✒️ Sharath Chandra S #Databricks #DataEngineering #MachineLearning #BigData #BusinessIntelligence

Unity Catalog in Databricks: Simplifying Data Access Management! Unity Catalog offers a unified governance solution that brings streamlined data access, security, and auditing capabilities to Databricks, making it a game-changer for managing data across your organization. #Key Advantages: -Centralized Data Governance: Manages access control and security policies centrally for all data assets. - Granular Access Control: Offers fine-grained permissions at table, row, and column levels, ensuring that users only access the data they’re authorized to view. - Automated Data Lineage: Tracks lineage across tables and queries to enhance traceability, allowing better data understanding and auditing. - Seamless Collaboration: Facilitates data sharing across teams and departments while maintaining strict security. # How to Create & Manage Unity Catalog: 1.Set Up a Unity Catalog Metastore: Link your metastore to cloud storage in Databricks to centralize all your organization’s data assets. 2. Assign Roles and Permissions: Use Data Access Control Lists (ACLs) to assign user roles and define permissions. 3.Define Ownership: Establish data ownership policies to maintain control over data access. 4.Enable Data Lineage: Turn on data lineage features to trace data flow and dependency. #Benefits for Data Access Management: With Unity Catalog, organizations can efficiently manage access to data, ensuring security compliance, enhancing productivity, and facilitating seamless collaboration. Its centralized control makes data governance straightforward, helping organizations stay compliant with regulatory standards while enabling self-service analytics. Unity Catalog empowers data teams to securely share and explore data, making it a key component for any data-driven organization. If you find this useful, please repost! 🌟 [Documentation Link](https://lnkd.in/dZTsYqkB) Join Live Batch: [https://lnkd.in/dCAcwXv6) Join ADE Group: [https://lnkd.in/dre5hfRq) Blog: [https://lnkd.in/dUjrZJCH) Follow Satish Mandale for more content! #DataGovernance #UnityCatalog #Databricks #DataSecurity #DataManagement #BigData #Analytics #DataEngineering #AzureDatabricks #DataAnalytics #QuantumTech

Data Governance.

𝗛𝗮𝘃𝗶𝗻𝗴 𝘁𝗿𝗼𝘂𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗦𝗤𝗟 𝗝𝗢𝗜𝗡𝘀? 𝗟𝗲𝘁’𝘀 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗰𝗹𝗲𝗮𝗿 𝗼𝗻𝗰𝗲 𝗮𝗻𝗱 𝗳𝗼𝗿 𝗮𝗹𝗹. If you’re working with data, understanding joins is a non-negotiable skill. Here’s a refined, practical guide to demystify them. 𝗧𝗵𝗲 𝗙𝗼𝘂𝗿 𝗖𝗼𝗿𝗲 𝗝𝗢𝗜𝗡𝘀, 𝗗𝗲𝗰𝗼𝗱𝗲𝗱: 1. INNER JOIN Retrieves only the records that match in both tables. Example: Employees who have recorded sales. 2. LEFT JOIN Shows all records from the left table, plus any matches from the right table. If no match, returns NULL. Example: All employees, even if they have no recorded sales. 3. RIGHT JOIN Shows all records from the right table, plus any matches from the left table. If no match, returns NULL. Example: All sales, regardless of whether there’s an employee listed. 4. FULL OUTER JOIN Displays all records from both tables, whether they match or not. If no match, fills with NULL. Example: All employees and all sales, connected or not. 𝗖𝗼𝗱𝗲 𝗧𝗵𝗲𝗺 𝗪𝗶𝘁𝗵 𝗖𝗹𝗮𝗿𝗶𝘁𝘆: Suppose we have two tables: Employees (emp_id, emp_name) and Sales (emp_id, sale_amount). Here’s how you’d apply each join: INNER JOIN SELECT Employees.emp_name, Sales.sale_amount FROM Employees INNER JOIN Sales ON Employees.emp_id = Sales.emp_id; LEFT JOIN SELECT Employees.emp_name, Sales.sale_amount FROM Employees LEFT JOIN Sales ON Employees.emp_id = Sales.emp_id; RIGHT JOIN SELECT Employees.emp_name, Sales.sale_amount FROM Employees RIGHT JOIN Sales ON Employees.emp_id = Sales.emp_id; FULL OUTER JOIN SELECT Employees.emp_name, Sales.sale_amount FROM Employees FULL OUTER JOIN Sales ON Employees.emp_id = Sales.emp_id; 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗝𝗢𝗜𝗡𝘀 𝗶𝗻 𝗦𝗤𝗟 𝗰𝗮𝗻 𝗯𝗲 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸. Once you understand them, SQL will become a far more powerful tool in your hands. Keep practicing, and it’ll soon become second nature! --- 📕 400 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/gv9yvfdd 📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://lnkd.in/gPrWQ8is 📙 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝘆: https://lnkd.in/gHSDtsmA 📗 45 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗕𝗼𝗼𝗸𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗡𝗲𝗲𝗱𝘀: https://lnkd.in/ghBXQfPc --- Join What's app channel for jobs updates: https://lnkd.in/gu8_ERtK

🚀 🏛 𝐀𝐩𝐚𝐜𝐡𝐞 𝐒𝐩𝐚𝐫𝐤 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: 𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐒𝐩𝐚𝐫𝐤 𝐖𝐨𝐫𝐤 ?🏛🧑💻 Spark is a framework that helps us to parallel data processing using In-memory persistence. In-memory refers to the RAM of the computer nodes within the Spark cluster🧑💻 1️⃣ 𝐒𝐩𝐚𝐫𝐤 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧 📤 ◾When you submit a Spark job, the Driver process starts. ◾The Driver requests resources from the Cluster Manager (YARN, Mesos, or Kubernetes). ◾ Cluster Manager allocates Worker Nodes to the job. 2️⃣ 𝐃𝐫𝐢𝐯𝐞𝐫 𝐍𝐨𝐝𝐞 𝐈𝐧𝐢𝐭𝐢𝐚𝐭𝐢𝐨𝐧 🧠 ◾Driver: Acts as the brain of Spark, responsible for: ◾ Creating the Spark Session. ◾Dividing work into tasks and stages. ◾Scheduling tasks across executors on Worker nodes. 3️⃣  𝐖𝐨𝐫𝐤𝐞𝐫 𝐍𝐨𝐝𝐞𝐬 𝐚𝐧𝐝 𝐄𝐱𝐞𝐜𝐮𝐭𝐨𝐫𝐬 ⚙️ ◾ Each Worker Node runs Executors, which are processes that: ◾Execute tasks assigned by the Driver. ◾Store data (in memory or on disk) for processing. 📊 Executors break the data into partitions, with each partition assigned to one or more cores for parallel processing. 4️⃣  𝐓𝐚𝐬𝐤 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧  📈 ◾Tasks are divided into Transformations (like map(), filter()) and Actions (like count(), collect()). 📝 Transformations are lazy and don't execute immediately. They create a DAG (Directed Acyclic Graph) of jobs to be optimized. 5️⃣ 𝐃𝐀𝐆 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞𝐫 & 𝐒𝐭𝐚𝐠𝐞 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 🎯 ◾The DAG Scheduler breaks the job into stages based on data shuffling requirements. ◾Tasks within a stage run in parallel across executors, processing data from partitions. 6️⃣ 𝐓𝐚𝐬𝐤 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 & 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 💾 ◾Executors process each task and store the results in memory or disk based on configuration. ◾You can cache frequently used data to speed up future computations (stored in on-heap or off-heap memory). 7️⃣ 𝐀𝐜𝐭𝐢𝐨𝐧𝐬 𝐓𝐫𝐢𝐠𝐠𝐞𝐫 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 ⚡ ◾When an Action (like collect() or write()) is called, the DAG is executed. ◾Data is shuffled between nodes if needed, and results are sent back to the Driver. 8️⃣  𝐉𝐨𝐛 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐢𝐨𝐧 & 𝐂𝐥𝐞𝐚𝐧𝐮𝐩  ✅ ◾Once all tasks complete, the final results are returned to the Driver. ◾Executors are cleaned up, releasing memory and resources. In the below picture, 1 --> Job submission 2 --> Communication between the Spark Master node and Cluster Manager 3 --> Resource Allocation and Task Scheduling 4 --> Heartbeat signals from the worker node to the Driver Program Tagging Anurag Sharma Abhinav Singh Vishnu Reddy Sagar Prajapati Riya Khandelwal Ajay Kadiyala Harshita Shukla Harshit Bhadiyadra Vaishnavi MURALIDHAR Devikrishna R 🇮🇳 💎Thodupunuri Bharath Shubham Kumar Shubham Wadekar for better reach. #SQL #DatabaseManagement #SQLInterviewPrep #DataSkills #Coding #DataScience #DataAnalysis #daatengineer #azuredataengineer #bigdata #pyspark #python #databricks #ApacheSpark #pysparkcodingquestions #Azuredatafactory #microsoftfabric #deltalake #deltaengine #spark

How can we automate our Databricks solutions using DABs ( Databricks Asset Bundles ) ? Databricks Asset Bundles streamline managing configurations, deployments, and resources across development stages within the Databricks environment. How to understand its Lifecycle & Workflow: - We can Create a Bundle using default templates, custom templates, or create manually. We can use databricks bundle init to initialize a bundle. - We have to Populate Configuration Files by Defining settings for targets (e.g., development, staging, production), artifact names, and job details. - Validate Configuration Files by Running databricks bundle validate to confirm settings are deployable. - We can Deploy the Bundle by Specifying the deployment target (e.g., dev, prod) in the configuration using the command: databricks bundle deploy - Run Jobs or Pipelines using Command databricks bundle run <job_key> to run a job within the default target or specify another target using t. - We Destroy the Bundle using command databricks bundle destroy to Deletes all jobs, pipelines, and artifacts from the bundle configuration. Real-Life Example - Databricks Asset Bundles (DABs) allow teams to automate workflows by defining and managing data pipelines. - For instance, a retail company can automate daily sales data processing by creating a DAB to ingest data from CSV files and APIs, transform it with Spark jobs, and load it into a data warehouse. - By deploying the bundle, the team can schedule jobs to run automatically each night while utilizing monitoring and alerts for quick issue resolution #Databricks #DataEngineering #WhatsTheData

I've worked with SQL for 10 years, and here are 7 data processing patterns that I've seen repeatedly: 1. Get top/bottom n rows from a group of rows You can get the top/bottom n salaries from an employee table. * Use ROW_NUMBER to rank the rows and a WHERE/QUALIFY clause to filter the n rows * Be mindful of the differences between RANK/DENSE_RANK & ROW_NUMBER * If you only need the top/bottom n, you can use COUNT_IF (if your DB supports it) You will often have to find the top n rows out of a set of rows (this is more frequently done in interviews). 2. Create columns for individual dimensional values (aka PIVOT) If you have worked with business folks, you know they love Excel's pivot function! * Pivoting dimension values to columns is incredibly helpful in the visual analysis of data * In SQL you can do this using SUM(CASE WHEN dim_col='dim_val_1' THEN metric_to_sum ELSE 0 END) for each dim value Most modern databases have included PIVOT as a native function. 3. Compare metrics to their value in a previous time period Most analytical dashboards show trends (i.e., comparison of a value to its previous period) * Open any metric tracking dashboard, and you will see a big number up top * The big number will have a /- sign with a percentage change next to it * In SQL, you can do this by GROUP BY & ORDER BY by the time period and using LAG to compare the current to the previous period The metrics for which you have to compare values are usually critical metrics like revenue, churn, etc 4. Given two versions of the same table, find the new/updated/deleted rows When testing a data pipeline, you'll often end up comparing the data you create to production data * You can check the number of rows, column types, etc * But to truly understand the data changes, you need to compare the values Assuming your dataset has a natural key, you can use hash (see screenshot). 5. Get data as of a specific period Often, with late arriving/processed data, you will run into issues of downstream consumers seeing changing metrics. * You can have 2 columns in your table: event timestamp & processing timestamp The query SELECT * FROM table WHERE event_ts = 'day1' AND proc_ts = 'day1 1', says give me data for day1 as of day2(aka time of processing) 6. Get data from a table that is not in another table You will often need to get data from a table that is not present in another table * If your tables share a key column, use the LEFT ANTI JOIN, else use the EXCEPT function 7. Create a series of numbers/dates to create reports Often, for reporting, you will need to show the metrics for all the dates even when you do not have any metrics for that date. * With GENERATE_SERIES, you can generate a series of numbers or dates * Left join the metrics data to this table More patterns > https://lnkd.in/e2TryF8Z - Please let me know what you think in the comments below. Also, follow me for more actionable data content #SQL #data #analytics