Divya Paidimalla’s Post

Day 30 of our #ADF Series: Hadoop Hive Activity 🐝 Welcome to Day 30! Today, we’re diving into the Hadoop Hive Activity in Azure Data Factory, a great way to integrate big data processing with your pipelines. ✨ What is Hadoop Hive Activity? The Hadoop Hive Activity enables you to execute Hive queries on a Hadoop cluster directly from Azure Data Factory. This is particularly useful when you need to process or transform large datasets stored in Hadoop before using them in your downstream workflows. 📌 Real-World Example: Imagine you're working with a Hadoop cluster that stores user behavior logs for an e-commerce platform. Using the Hadoop Hive Activity, you can run HiveQL scripts to aggregate purchase patterns and filter important data for further analysis or visualization in your ADF pipeline. 📈 Common Use Cases: Querying and transforming data in Hadoop clusters using HiveQL scripts. Aggregating large datasets for reporting and analytics. Preparing data for further processing in cloud-based systems like Azure Synapse or Data Lake. ⚠️ Limitations and Workarounds: Limitation: Requires a linked service to the Hadoop cluster and appropriate configurations. Workaround: Ensure the cluster is accessible from Azure and the service principal or authentication method is correctly set up. Limitation: Execution may be slower for extremely large datasets. Workaround: Optimize HiveQL scripts and use partitioning to process data more efficiently. 💡 Pro Tip: Use the Hadoop Hive Activity in combination with Copy Activity to pull the processed data into Azure Data Lake or Synapse Analytics for downstream tasks. Have you leveraged Hadoop Hive Activity to manage your big data workflows? Share your experiences in the comments! 👇 Ref Doc: https://lnkd.in/g_sn3uQa #AzureDataFactory #DataEngineering #ETL #BigData #HadoopHiveActivity

Thinking about transitioning from #Hadoop to Databricks? In the next few days, we’ll publish our blog that outlines the exact path from Hadoop to Databricks, but in the meantine, check our previous articles breaking down the what, why, and how of this modernization choice. 🚨Spoiler🚨: Migrating to Databricks saves time, money, and opens doors to real-time business intelligence and AI innovation! 🔍 Part 1: Introduction to Hadoop vs Databricks - Hadoop’s challenges—high operational costs, inefficiencies, and DevOps nightmares—are driving businesses to seek better alternatives. We explain why Databricks is the superior solution for cost, performance, and scalability. 🔗 https://lnkd.in/dvU-N8VY 🔑 Part 2: The Migration Journey - Breaking down the complexities of the move, we discuss the essential steps to ensure a seamless migration, from handling Spark workloads to converting legacy data processes. 🔗 https://lnkd.in/gts5HmEt 💡 Part 3: Advanced Data Management & Governance - Dive into how Databricks transforms data processing, enhances governance, and enables the power of real-time insights—all while supporting AI/ML. 🔗 https://lnkd.in/dCt_m3Tz 👉 What are your thoughts on migrating from Hadoop to Databricks? Let’s discuss in the comments! 💬 #Databricks #DataEngineering #Migration #Cloud #Lakehouse #SunnyData Kailash ( Kai) Thapa | Santiago Carrera | Allen M. Becker, MBA, GB | Josue A. Bogran | Ernest P.

𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗚𝗿𝗼𝘄𝘁𝗵: 𝗚𝗹𝗼𝗯𝗮𝗹 𝗛𝗮𝗱𝗼𝗼𝗽 𝗮𝗻𝗱 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗧𝗿𝗲𝗻𝗱𝘀 The Global Hadoop and Big Data Analysis market is expected to grow at a CAGR of 12.6% from 2023 to 2030, driven by the increasing adoption of big data solutions across various sectors, including finance, healthcare, retail, and telecommunications. This growth is attributed to the rising demand for advanced analytics, cloud-based solutions, and the ongoing digital transformation across industries. 𝗖𝗹𝗶𝗰𝗸 𝗵𝗲𝗿𝗲 𝘁𝗼 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗳𝗿𝗲𝗲 𝗦𝗮𝗺𝗽𝗹𝗲 𝗥𝗲𝗽𝗼𝗿𝘁: https://lnkd.in/dU6Df5ZK The global Hadoop and Big Data Analysis market is experiencing rapid growth, driven by the increasing need for data-driven insights across industries. With organizations generating massive amounts of data, Hadoop's open-source framework is playing a crucial role in efficiently storing and processing large data sets. The market is expected to expand significantly in the coming years, fueled by advancements in machine learning, AI, and cloud computing. Companies that harness the power of big data analytics are gaining a competitive edge through better decision-making and improved customer experiences. 𝗕𝘂𝘆 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗥𝗲𝗽𝗼𝗿𝘁: https://lnkd.in/dxtegWuB Top Key Players: Cloudera | Hortonworks | Hadapt | Amazon Web Services (AWS) | Outerthought | MapR Technologies, acquired by Hewlett Packard Enterprise company in 2019 | Platform Computing | Karmasphere | Greenplum Database by VMware | HStreaming | Pentaho | Zettaset #BigData #Hadoop #DataAnalytics #MarketResearch #TechTrends #BusinessGrowth #DataScience

Title : How does Azure Data Lake Analytics simplify big data processing tasks compared to managing Hadoop clusters manually? Azure Data Lake Analytics offers a simplified approach to big data processing tasks compared to managing Hadoop clusters manually. Here's how: Serverless Architecture: With Azure Data Lake Analytics, you don't need to provision or manage Hadoop clusters manually. It follows a serverless architecture, where you only pay for the processing power and resources consumed during job execution. This eliminates the overhead of cluster management, including provisioning, scaling, and monitoring. Scalability and Performance: Azure Data Lake Analytics automatically scales resources based on the workload, allowing you to process large volumes of data efficiently. It leverages the underlying Azure infrastructure to dynamically allocate resources as needed, ensuring optimal performance without the need for manual tuning or optimization. Integration with Azure Data Lake Storage: Azure Data Lake Analytics seamlessly integrates with Azure Data Lake Storage, providing a unified platform for storing and processing big data. This integration simplifies data management and eliminates data movement overhead, as data can be processed directly from the storage layer without the need for additional data transfers. Familiar Query Language: Azure Data Lake Analytics supports familiar query languages such as U-SQL, which combines the power of SQL with the flexibility of C#. This allows developers and data engineers to leverage their existing skills and tools to write and debug complex data processing jobs, streamlining development efforts and reducing the learning curve. Built-in Monitoring and Management: Azure Data Lake Analytics provides built-in monitoring and management capabilities, allowing you to track job execution, monitor resource utilization, and troubleshoot issues in real-time. You can view job history, performance metrics, and execution logs directly from the Azure portal, enabling proactive management and optimization of big data processing tasks. Overall, Azure Data Lake Analytics simplifies big data processing tasks by offering a serverless architecture, seamless integration with Azure Data Lake Storage, support for familiar query languages, and built-in monitoring and management capabilities. It enables organizations to focus on deriving insights from their data without worrying about the complexities of managing Hadoop clusters manually. #Azure #BigData #DataAnalytics #DataLake #DataProcessing #CloudComputing #AzureDataLakeAnalytics #Hadoop #Serverless

Big Data : Navigating the Digital Ocean of Information As you all are aware, Big Data is in demand currently, wherein enterprises moving towards Big Data oriented data architectures My article regarding the basics of #bigdata has been published on C# Corner Do have a look and let me know your thoughts #bigdata #artificialintelligence #hadoop #azure #databricks #bigdataengineer #bigdataanalytics #basics #interviewprep #interviewsuccess #article #csharpcorner

𝗣𝗼𝗹𝘆𝗕𝗮𝘀𝗲 is a feature in Azure Synapse Analytics that allows you to access and query external data stored in Azure Blob Storage or Azure Data Lake Store directly using T-SQL. It enables you to perform Extract, Load, and Transform (ELT) operations efficiently. It does require going through a handful of steps: 1.Create Master Key for database 2.Create Database Scoped Credential 3.Create External Data Source  4.Create External File Format 5.Create schema 6.Create External Table 7.Query data.   𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗼𝗳 𝗣𝗼𝗹𝘆𝗕𝗮𝘀𝗲: 1.𝗗𝗮𝘁𝗮 𝗩𝗶𝗿𝘁𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: PolyBase allows you to query external data without moving it into the data warehouse. This means you can access and join external data with relational tables in your SQL pool2. 2.𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗧𝗮𝗯𝗹𝗲𝘀: You can create external tables that reference data stored in Azure Blob Storage or Azure Data Lake Store. These tables can be queried just like regular tables in your SQL pool. 3.𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗙𝗼𝗿𝗺𝗮𝘁𝘀: PolyBase supports various file formats, including delimited text files (UTF-8 and UTF-16), Hadoop file formats (RC File, ORC, Parquet), and compressed files (Gzip, Snappy). 4.𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: It leverages the massively parallel processing (MPP) architecture of Azure Synapse Analytics, making it highly scalable and efficient for large data sets. 5.𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗘𝗧𝗟: By using PolyBase, you can minimize the need for traditional Extract, Transform, and Load (ETL) processes, as data can be loaded directly into staging tables and transformed within the SQL pool

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗢𝗟𝗔𝗣 𝘃𝘀. 𝗢𝗟𝗧𝗣 𝗶𝗻 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮: 𝗔 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) are two different types of database systems that serve distinct purposes in the context of big data. 𝗣𝗿𝗶𝗺𝗮𝗿𝘆 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 𝗼𝗳 𝗢𝗟𝗧𝗣 𝗮𝗻𝗱 𝗢𝗟𝗔𝗣 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀? • OLTP databases support daily transactions and operational tasks . • OLAP databases assist with complex analysis, reporting, and decision-making. 𝗥𝗲𝗮𝗹-𝗟𝗶𝗳𝗲 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗮 𝗹𝗮𝗿𝗴𝗲 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗹𝗶𝗸𝗲 𝗔𝗺𝗮𝘇𝗼𝗻: • OLTP: Every time a customer places an order, the details (product, price, customer info, payment status) are instantly recorded in an OLTP database. The system ensures that millions of transactions can be processed simultaneously without delays. • OLAP: At the end of the day, the data from thousands of transactions is transferred to an OLAP system, enabling analysts to run queries, understand trends, and forecast demand. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀 • In modern big data environments, both OLAP and OLTP systems often coexist. OLTP databases handle real-time data ingestion, while OLAP systems are used to generate insights from that data. Technologies like Apache Hadoop, Apache Spark, and cloud-based data warehouses (e.g., Amazon Redshift, Google BigQuery, Hive,Azure Synapse) often play a role in managing OLAP workloads at scale. 𝗘𝗧𝗟: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗻𝗴 𝗢𝗟𝗧𝗣 𝗮𝗻𝗱 𝗢𝗟𝗔𝗣 • Data from OLTP systems is transferred to OLAP databases through a process called ETL (Extract, Transform, Load), enabling powerful data analysis. #BigData #OLAP #OLTP #DataEngineer #DataAnalytics #ETL #DataWarehouse #AzureSynapse #cloud #LinkedinLearning #TechInsights #AmazonRedshift #GoogleBigQuery #Hive

RDBMS vs. Cloudera for Big Data: Which is Right for Your Company? Big data isn't just a buzzword—it's a game-changer. But harnessing its power requires the right tools. Traditional relational database management systems (RDBMS) have long been the backbone of data storage. However, when the "three Vs" of big data (volume, velocity, and variety) come into play, RDBMS solutions often hit their limits. Here's a quick comparison to help you make an informed decision: RDBMS Strengths: Structured data: Excels at handling well-organized data in rows and columns. ACID compliance: Ensures data integrity with atomicity, consistency, isolation, and durability. Mature technology: Well-established with a wide range of tools and resources. RDBMS Limitations: Scalability: Can struggle with the sheer volume and variety of big data. Flexibility: Not designed for unstructured or semi-structured data (e.g., social media posts, sensor data). Real-time processing: May not be ideal for analyzing data as it's generated. Cloudera Strengths: Scalability: Built on Hadoop, it can handle massive datasets across distributed clusters. Flexibility: Easily processes structured, unstructured, and semi-structured data. Real-time analytics: Offers tools for stream processing and machine learning. Ecosystem: Provides a suite of integrated tools for data ingestion, storage, processing, and analysis. Cloudera Considerations: Complexity: Requires specialized skills and expertise to set up and manage. Cost: Can be more expensive than traditional RDBMS solutions. The Bottom Line If your company is dealing with large volumes of diverse data and needs to extract insights in real-time, Cloudera's big data platform could be the right choice. It offers the scalability, flexibility, and analytics capabilities that RDBMS solutions often lack. However, if your data is primarily structured and your needs are less demanding, a traditional RDBMS might still be a suitable and cost-effective option. Let's Connect! Curious about which solution is right for your specific big data challenges? I'm happy to discuss your needs and share more insights. Feel free to reach out! #BigData #DataAnalytics #Cloudera #RDBMS #DataScience

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 💡 𝗙𝗶𝗿𝘀𝘁 𝗣𝗮𝗿𝘁 👉 https://lnkd.in/dybFdgKs 𝗦𝗲𝗰𝗼𝗻𝗱 𝗣𝗮𝗿𝘁 👉 https://lnkd.in/gN62fjw7 𝗧𝗵𝗶𝗿𝗱 𝗣𝗮𝗿𝘁 👉 https://lnkd.in/dBHWaN5X 𝗙𝗼𝘂𝗿𝘁𝗵 𝗣𝗮𝗿𝘁 & 𝗟𝗮𝘀𝘁 𝗣𝗮𝗿𝘁 👇 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗛𝗮𝗱𝗼𝗼𝗽, 𝗦𝗽𝗮𝗿𝗸, 𝗮𝗻𝗱 𝗛𝗗𝗙𝗦 The true power of big data technologies emerges when these tools are used together. HDFS provides robust storage, Hadoop offers reliable distributed computing, and Spark brings speed and versatility to data processing, allowing organizations to efficiently store, process, and analyze massive datasets. 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲: 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🔹 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: Raw data is uploaded into the HDFS from various sources, such as log files, social media feeds, and transactional databases. 🔹 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: The data is processed in parallel across the cluster using Hadoop's MapReduce or Spark. This stage may involve filtering, aggregation, and data transformation. 🔹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: With Spark's advanced analytics capabilities, data scientists can perform complex analyses, create machine learning models, and visualize results in near real-time. 🔹 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: Processed data can be stored back into HDFS or transferred to other storage systems for further analysis and reporting. 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 Big Data technologies like Hadoop, Spark, and HDFS have revolutionized how we handle and interpret vast amounts of data, enabling organizations to unlock new insights and stay competitive. Understanding and utilizing these technologies is crucial for navigating today's data landscape. Embrace the power of Hadoop, Spark, and HDFS to transform your data into actionable insights and drive innovation. #BigData #DataAnalytics #Hadoop #ApacheSpark #HDFS #DataScience #MachineLearning #DataEngineering #DataPipeline #TechInnovation

𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗠𝗮𝗿𝗸𝗲𝘁 is expected to grow from USD 72.87 billion in 2023 to USD 110.57 billion by 2030, at a CAGR of 5.89% during the forecast period. 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗮𝗺𝗽𝗹𝗲 📚 https://lnkd.in/gP_kSiCf ◾ Rapid increase in consumer and machine data developments. Big Data is referred as the collection of data sets so large and complex that it is not possible to process it in traditional way. ◾ Big Data infrastructure is the cornerstone of the enterprises to sort, store, process and analyze the large data sets. An enterprise generates data in large volume with high velocity and veracity which cannot be stored and processed traditionally and this raises the demand for emerging data intensive analytical technology. ◾ Big Data Infrastructure Market is, the usage of Hadoop for processing, storing and analyzing has been increased compared to other tool such as NoSQL and massive parallel processing. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗥𝗲𝗽𝗼𝗿𝘁 👉 https://lnkd.in/gdwcpZMS 𝗧𝗼𝗽 𝗞𝗲𝘆 𝗣𝗹𝗮𝘆𝗲𝗿𝘀 𝗜𝗻𝗰𝗹𝘂𝗱𝗲 : Cloudera Hortonworks MAPR IBM Oracle Teradata Microsoft Amazon Web Services (AWS) Google SAP Dell Technologies Hewlett Packard Enterprise Cisco Intel Corporation NVIDIA Splunk NetApp Informatica MongoDB Snowflake #bigdata #datainfrastructure #dataanalytics #datascience #hadoop #spark #nosql #dataengineering #cloudcomputing #machinelearning