Kautuk Pandey

🚀 Exciting day at Snowflake Data Cloud World Tour Mumbai 2024! ❄️ I had the privilege of attending the Snowflake Data Cloud World Tour in Mumbai, and here are some key takeaways from insightful sessions on the future of data and AI: 📊 Open Table Formats & Iceberg Support Exploring integration possibilities with Apache Iceberg and Polaris Discussions on ACID compliance, performance optimization, and data evolution 🛡️ Snowflake Horizon Unveiling a vision for unified data governance and security across clouds Concepts for simplifying management of data and apps from a centralized interface 🤖 Generative AI & LLMs Introduction to the Snowflake Arctic family of Large Language Models (LLMs) Exploring Snowflake Cortex as a pathway to democratize AI Emphasis on the "AI to Data, not Data to AI" approach for enterprises 🔒 Data Governance & Security Highlighting the importance of unified security and compliance measures Showcasing potential applications of Dynamic Data Masking for data protection 💻 Snowpark Container Service & ML API Demonstrations of running AI and full-stack apps within the Snowflake ecosystem Exploring possibilities for direct ML model execution and predictions 🔗 Native Apps & Data Sharing Discussing streamlined approaches to building and sharing data applications Insights into enhancing operational collaborations through data sharing 📄 Document AI & Unstructured Data Previewing tools aimed at extracting insights from unstructured data The event showcased Snowflake's vision for innovation, open standards, and efficient data management. It was fascinating to see the proposed solutions for various data challenges, from governance to AI implementation. Kudos to the speakers, including Sanjay Deshmukh Vijayant Rai Rahul Jain 🇮🇳 Kamesh Sampath and Sumeet Tandure Excited to contemplate how these concepts might shape our future data strategies! #SnowflakeWorldTour #DataCloud #AI #DataGovernance #Innovation

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Uber's Data Platform is an inspiration for many Data Engineers. It hosts around 👀 𝟭.𝟱 𝗲𝘅𝗮𝗯𝘆𝘁𝗲𝘀 of Apache Hadoop Distributed File System (HDFS). Uber has built 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 to handle such a huge amount of data, on which thousands of #Spark jobs are running and hundreds of concurrent users are running queries. ✨ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵? ☁ Data Mesh is a cloud resources management service that organizes data resources in an organization-centric hierarchical manner.  𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵: 1. 𝗗𝗼𝗺𝗮𝗶𝗻: A hierarchical unit of organizing all the resources in an organization or team. In data mesh terms, domains can be both at the organization level or at a sub-organization/team level. 2. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Resource containers, which are a unit of access control, resource grouping, and cost accounting. 3. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: A logical container for data (DB/tables/files) belonging to the same category e.g., raw data, tables for internal consumption or modeled tables for external broad access. 4. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: The storage buckets which are a unit of access control, cost accounting, geographic location, storage class (hot, warm, cold), data lifecycle policies (TTL, legal holds, etc.), and data replication (to either standby store or archival store). 5. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁: A curated, discoverable, and usable representation of data, which includes the Hive tables, BI dashboards, workflow pipelines, etc. Read more about this here: https://lnkd.in/gkt7k5jJ #Uber #DataEngineering #DataMesh #architecture #cloud Arun M. Abhi Khune Sahana Bhat

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Using dynamic logging in Kafka Generally when starting kafka broker service, we provide log4j file for error logging. log4j configuration is generally given using KAFKA_LOG4J_OPTS environment variable. By default, in Confluent, Kafka Broker log4j points to $CONFLUENT_HOME/etc/server/log4j.properties. logging levels can be changed to DEBUG from default setting of INFO. This enables printing of additional information in logs to be used for debugging of issues. This setting is static and cannot be changed in runtime. any change in logging level using this method requires a restart of Kafka broker.  Kafka offers a way of increasing logging dynamically. Dynamic logging was introduced in kafka 2.4.0 version as part of KIP-412(https://lnkd.in/g7pNA3JD).  To view all the dynamic configuration for broker,you need to use kafka-configs to        kafka-configs --describe --bootstrap-server `broker_host`:`port` --entity-type broker-loggers --entity-name `broker_id`  One of the metrics i like to enable in debug mode for client issues is kafka-request-logger. To enable this you need to run the below  kafka-configs --alter --bootstrap-server `broker_host`:`port` --entity-type broker-loggers --entity-name `broker_id` --add-config "kafka.request.logger=DEBUG" Kafka Request Logger is used for logging request received by Kafka broker and when debug mode is enabled each entry includes the principal, client host/ip, client version, type of security protocol, client software being used and software version.

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Good to see our team exploring these insights from the FinOps Foundation! As we continue to empower businesses with better cloud cost management, understanding unit economics is pivotal to scaling responsibly. What are your thoughts on tying cloud costs to business value? Let’s keep the conversation going! #finops #uniteconomics #cloudcosts #profitability

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All Data Engineers - I Would like to share learning of different types of languages in Data World today i learnt 😎 : DDL --> Data Definition Language. Ex: Create, drop, alter table DML --> Data Manipulation Language. Ex: update, insert to table All these years most of us aware above but interestingly there are few others are there.. DCL --> Data Control Language. Ex: Grant and revoke TCL --> Transaction Control Language. Ex: Commit, rollback DQL --> Data Query Language. Ex: Select How many of you thought there are only two types DDL,DML only?

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𝐃𝐞𝐚𝐫 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 🔥 Can you solve this simple PySpark Transformation Challenge? 📣 𝐈𝐧𝐟𝐨: You have a DataFrame that contains employee phone numbers where multiple rows may correspond to the same employee id. Your goal is to aggregate all phone numbers for each employee id. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐃𝐚𝐭𝐚𝐟𝐫𝐚𝐦𝐞 (Input Data): # 𝘚𝘢𝘮𝘱𝘭𝘦 𝘥𝘢𝘵𝘢 𝘥𝘢𝘵𝘢 = [   (1, '8956756457'),   (1, '9345636785'),   (2, '9945675647'),   (3, '8767345678'),   (3, '88745921785') ] # 𝘊𝘳𝘦𝘢𝘵𝘦 𝘋𝘢𝘵𝘢𝘍𝘳𝘢𝘮𝘦 𝘤𝘰𝘭𝘶𝘮𝘯𝘴 = ["𝘪𝘥", "𝘱𝘩𝘰𝘯𝘦_𝘯𝘶𝘮𝘣𝘦𝘳"] 𝘥𝘧 = 𝘴𝘱𝘢𝘳𝘬.𝘤𝘳𝘦𝘢𝘵𝘦𝘋𝘢𝘵𝘢𝘍𝘳𝘢𝘮𝘦(𝘥𝘢𝘵𝘢, 𝘤𝘰𝘭𝘶𝘮𝘯𝘴) 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐝𝐞? (Expected Data) 𝘥𝘧_𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘥 = ????? Let me know the answer in the comments 😀 #pyspark #dataengineering

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Architecture of Apache Spark!! Interviewer: Could you explain the architecture of Apache Spark? Candidate: Sure! Apache Spark has a master-slave architecture that consists of a Driver, Executors, and a Cluster Manager. Let me break it down for you! Driver Program: - The Driver is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the SparkContext, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. Executors: - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. Cluster Manager: - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - Standalone Cluster Manager: A simple built-in cluster manager.  - Apache Mesos: A general cluster manager that can run Hadoop MapReduce and other applications.  - Hadoop YARN: The resource management layer of Hadoop.  - Kubernetes: An open-source platform for automating deployment, scaling, and operations of application containers. Interviewer: Great. Could you explain how the Driver and Executors interact during a Spark job execution? Candidate: Absolutely. 1. Job Submission:  - The user submits a Spark application using the SparkContext in the driver program. 2. DAG Construction:  - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. Task Scheduling:  - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. Task Execution:  - The cluster manager allocates resources and schedules the tasks on available executors.  - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. Result Collection:  - Executors send the results of their computations back to the driver.  - The driver program consolidates these results and performs any final actions required. Interviewer: You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into transformations and actions: - Transformations:  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - Actions:  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

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Types of Indexing in Apache Hudi Index TypeDescriptionUse CasesBloom IndexUtilizes Bloom filters to quickly determine the potential presence of a record in a file group, reducing the search space during upserts and deletes.- High Throughput Upserts/Deletes: Ideal for scenarios with frequent data modifications. - Large Datasets: Efficiently handles big data volumes by minimizing file scans.Global Bloom IndexAn extension of the Bloom Index that maintains Bloom filters across all file groups globally, enhancing lookup performance for massive datasets.- Massive Scale Operations: Suitable for extremely large datasets where maintaining global state significantly improves performance. - Distributed Environments: Enhances consistency and speed across distributed file groups.HBase IndexLeverages HBase to store and manage index data externally, providing low-latency record lookups and high scalability.- Low-Latency Requirements: Perfect for applications needing rapid access to specific records. - External Index Management: When integrating with HBase ecosystems for robust indexing solutions.Simple IndexA non-index-based approach that scans all file groups to locate records. Acts as a fallback when other indexing mechanisms are not enabled or suitable.- Fallback Mechanism: Ensures data operations can proceed even without specialized indexing. - Smaller Datasets: Acceptable for datasets where full scans do not significantly impact performance.In-Memory Hash IndexMaintains an in-memory hash table mapping record keys to their respective file paths, enabling quick lookups without disk I/O.- Fast Lookups: Best for applications requiring rapid access to records with minimal latency. - Memory-Abundant Environments: Suitable when ample memory resources are available to store the hash index.SQL Query Examples Using Apache Hudi Indexes Apache Hudi integrates seamlessly with Spark SQL, allowing you to specify indexing types during table creation or data write operations. Below are examples demonstrating how to leverage different indexing types in your SQL workflows. Example - Creating a Hudi Table with Bloom Index sql Copy code CREATE TABLE hudi_bloom_index_table ( id STRING, name STRING, age INT, city STRING ) USING hudi OPTIONS ( primaryKey = 'id', preCombineField = 'timestamp', hoodie.index.type = 'BLOOM' ); Explanation: hoodie.index.type = 'BLOOM': Specifies the use of Bloom Index for efficient record lookups during upserts and deletes.

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Converting a list to string, with a delimiter. Implementation of inbuilt mkString() function in SCALA. #SCALA #SCALADEVELOPER #BIGDATADEVELOPER #BIGDATA Karthik K. def main(args: Array[String]): Unit ={ //var a = "Tanuj" //lowerCase(a) var s = "" var l = List(1,2,3,4,5,6,7,8) for( i <- 0 until l.length) { if( i == l.length -1) { s = s.concat(l(i).toString) } else { s = s.concat(l(i).toString) s = s.concat(':'.toString) } } print("The elements of list when converted to string are:-" + s) } } OUTPUT:

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You asked for memes But on a serious note - the core differentiator of lakeflow vs. the rest of the Databricks suite is twofold; there is a GUI for building pipelines (nice!) which is presumably code-based (.yml /json see docs "declarative data pipelines") but where the Tasks you can implement are also pretty cool (e.g. Oracle CDC task, very nice). In Azure Data Factory, you get: - GUI for building pipelines - .yml under the hood - cool data processing capabilities for the Azure suite This is pretty much what Lakeflow is Different things - Integration with the rest of the Databricks platform (e.g. Unity Catalog, really cool) - Integrations with third party sources (something ADF has always done TERRIBLY) We know what ADF's strengths and weaknesses are. It is great for specific data processing tasks in Azure. it is not the way to unify your data platform. Bricksters now face a strange choice - do they unify with dbx workflows, lakeflow pipelines or something else? Personally, I also would have created a standalone streaming service rather than bundle it in to something ADF-like (there is precedent for this. Streaming is hard. Estuary, RedPanda, Confluent etc. all streaming focussed ELT vendors that think it's sufficiently hard to look like their products do and not ADF..) but I can see the short-term appeal #databricks #lakeflow #adf

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If you spend 5-10 mins on this post, then you can learn a really important topic in pyspark which a 1 hour video also cannot teach you this effectively. Lets say you have a 20 node spark cluster Each node is of size - 16 cpu cores / 64 gb RAM Let's say each node has 3 executors, with each executor of size - 5 cpu cores / 21 GB RAM => 1. What's the total capacity of cluster? We will have 20 * 3 = 60 executors Total CPU capacity: 60 * 5 = 300 cpu Cores Total Memory capacity: 60 * 21 = 1260 GB RAM => 2. How many parallel tasks can run on this cluster? We have 300 CPU cores, we can run 300 parallel tasks on this cluster. => 3. Let's say you requested for 4 executors then how many parallel tasks can run? so the capacity we got is 20 cpu cores 84 GB RAM so a total of 20 parallel tasks can run. => 4. Let's say we read a csv file of 10.1 GB stored in datalake and have to do some filtering of data, how many tasks will run? if we create a dataframe out of 10.1 GB file we will get 81 partitions in our dataframe. (will cover in my next post on how many partitions are created) so we have 81 partitions each of size 128 mb, the last partition will be a bit smaller. so our job will have 81 total tasks. but we have 20 cpu cores lets say each task takes around 10 second to process 128 mb data. so first 20 tasks run in parallel, once these 20 tasks are done the other 20 tasks are executed and so on... so totally 5 cycles, if we think the most ideal scenario. 10 sec + 10 sec + 10 sec + 10 sec + 8 sec first 4 cycles is to process 80 tasks all of 128 mb, last 8 sec is to process just one task of around 100 mb, so it takes little lesser but 19 cpu cores were free during this time. => 5. is there a possibility of, out of memory error in the above scenario? Each executor has 5 cpu cores and 21 gb ram. This 21 gb RAM is divided in various parts - 300 mb reserved memory, 40% user memory to store user defined variables/data. example hashmap 60% spark memory - this is divided 50:50 between storage memory and execution memory. so basically we are looking at execution memory and it will be around 28% roughly of the total memory allotted. so consider around 6 GB of 21 GB memory is meant for execution memory. per cpu core we have (6 GB / 5 cores) = 1.2 GB execution memory. That means our task can roughly handle around 1.2 GB of data. however, we are handling 128 mb so we are well under this range. I hope you liked the explanation :) My new big data batch is starting tomorrow. DM to know more. #bigdata #career #dataengineering #apachespark

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Curious how to use Apache Hudi & Iceberg tables in Databricks environment? Check out this hands-on blog by Dipankar Mazumdar, M.Sc 🥑 that shows how to use Apache XTable to interoperate between the 3 table formats in a lakehouse architecture. 🔗 Link: https://lnkd.in/dwTdBinz #dataengineering #softwareengineering

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During the 2010s, every new engine (Impala, Drill) would write blogs benchmarking against good'ol #ApacheHive instead of, say, MPP warehouses like Vertica. The 2020s equivalent seems to be beating up #ApacheSpark, shying away from comparing to #BigQuery or #Databricks or #Snowflake? Does anyone have some pointers to head-head (neutral) benchmarks, OSS vs commercial products on something > 1TB tpc-ds/tpc-h?

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Do you 𝑟𝑒𝑎𝑙𝑙𝑦 need 𝐝𝐛𝐭? dbt is on 🔥 and every consulting company now claims to know dbt (not all are created equal, ask me 😄), but should your organization jump on the bandwagon? 1️⃣ Do you lack data documentation? 📖 While dbt won't write the documentation for you, it enables teams to capture docs and expose them to end users via a Data Catalog of choice. 2️⃣ Would adding data quality or unit tests to your process help you detect issues before your end users? ✅ There are many ways you can perform Data Tests and there are lots of open source and paid options, but the first step is to actually do something. dbt also has packages like 𝐝𝐛𝐭-𝐞𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐬 so you don't have to reinvent the wheel and now you can do unit tests in addition to data tests using just dbt. 3️⃣ Is it hard for you to pin point the source of a bad calculation or understand the impact of a change to a source? 📊 dbt provides a lineage graph and you can enrich it with exposures which link data in your data warehouse to dashboards, ML models, etc. With dbt, it is relatively straight forward to find the impact of a change and find the cause of a production issue. 4️⃣ Do you have "black box" transformation processes where users have to "trust" things were done "right" and different teams use different tools? 🧩 It is common to see tools for 𝐈𝐓 and tools for "business" users. Traditional tools may have GUI interfaces and give some people the false sense of simplicity, but the biggest issue is that you have multiple people doing the same type of work using different tools. I am not suggesting there is no need for Excel or other business friendly tools, but I am saying that the bulk of your data transformation should be done in a consistent way. 5️⃣ Do you manually validate changes and manually deploy code to production or worse yet, you only have a single production database where you make changes directly? 💥 I shouldn't have to tell you how bad it is to change production directly. A bad query can take down the database for everyone or worse yet, deletes some data unintentionally. With dbt you can have isolated development environments, perform automated tests in isolation, and deploy code to production without manual intervention. Today, no one should be making changes without first committing code to git and promoting to production. When you implement good 𝐃𝐚𝐭𝐚𝐎𝐩𝐬 processes you can also enforce governance rules by using tools like 𝐝𝐛𝐭-𝐜𝐡𝐞𝐜𝐤𝐩𝐨𝐢𝐧𝐭 and SQLFluff. dbt is not right for everyone and there are other options out there, but there are many companies that can benefit from implementing dbt. I won't sugar coat it, you also need to change your mindset, process development is not trivial, and platform setup and management can be a pain which is why we created Datacoves, but implementing dbt correctly will help many companies both large and small. Are your data pipelines robust?

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How the Sort Merge Join Happens in the Apache Spark , Know the SMJ internals Sort-Merge Join (SMJ): In Apache Spark, Sort-Merge Join (SMJ) is the default join strategy for combining datasets based on a shared join key.  It excels at handling large datasets that might not entirely reside in memory on a single worker node. its execution phases: 1. Shuffle Phase: Spark partitions both input DataFrames according to the join key. This ensures records with matching join keys end up on the same worker node. Each partition is shuffled and sent across the network to the appropriate worker nodes based on the join key's hash value. 2. Sort Phase: Within each worker node, the shuffled partitions are sorted independently and in parallel using a stable sorting algorithm (like Timsort) to maintain the order of join keys. 3. Merge Phase: Worker nodes iterate through their sorted partitions simultaneously, performing a merge join operation. They compare the join keys of each DataFrame, and when a match is found, they combine the corresponding rows into a new joined row. This process continues until all matching join keys from both DataFrames have been processed. Example: Imagine you have two DataFrames: customers: (customer_id, name, city) orders: (order_id, customer_id, amount) You want to join them on the customer_id to get customer details along with their orders. Execution Steps: Shuffle: Both customers and orders are partitioned and shuffled based on customer_id. Sort: On each worker node, the shuffled partitions for customers and orders are sorted independently by customer_id. Merge: Worker nodes iterate through their sorted partitions, comparing customer_id values. When a match is found, a new row is created with data from both DataFrames (e.g., (customer_id, name, city, order_id, amount)). Key Points: SMJ works well for datasets that don't fit entirely in memory or where the join key has good cardinality (distinct values). It requires shuffling data, which can be network-intensive. Spark can automatically choose a more efficient join strategy like broadcast join if one DataFrame is significantly smaller than the other. Additional Considerations: You can configure the number of shuffle partitions using spark.sql.shuffle.partitions. SMJ can be optimized by using in-memory sorting if data fits in memory (spark.sql.sortMergeJoin.forceInMemory=true). For very large datasets, Spark might spill intermediate results to disk during shuffling and sorting, potentially impacting performance. TrendyTech #Spark, #BigData, #DataEngineering, #SoftwareEngineering

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Comprehensive Interview Questions on Apache Hive, Spark, Kafka, Airflow, Hudi, Flink, Iceberg 🎯 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗛𝗶𝘃𝗲 - What are the differences between internal and external tables in Hive, and when would you use each? - How do partitioning and bucketing improve query performance in Hive? - What are optimized joins in Hive, and how can you improve the efficiency of large table joins? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 - What is the difference between transformations and actions in Spark, and can you provide an example? - How can data partitioning be optimized for performance in Spark? - What is the difference between cache() and persist() in Spark, and when would you use each? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 - How does Kafka partitioning enable scalability and load balancing? - How does Kafka’s replication mechanism provide durability and fault tolerance? - How would you manage Kafka consumer rebalancing to minimize data loss? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 - What are dynamic DAGs in Airflow, and what benefits do they offer? - What are Airflow pools, and how do they help control task concurrency? - How do you implement time-based and event-based triggers for DAGs in Airflow? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗛𝘂𝗱𝗶 - How does Apache Hudi support incremental data processing in data lakes? - What are the different table types available in Apache Hudi, and how do they differ? - How does Hudi handle upserts (updates and inserts) at scale? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 - What is Apache Flink’s event-time processing, and how is it different from processing time? - How does Flink’s state management work for fault-tolerance in stream processing? - What are the different windowing functions available in Apache Flink? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗜𝗰𝗲𝗯𝗲𝗿𝗴 - How does Apache Iceberg enable schema evolution without breaking existing queries? - What is the purpose of partitioning in Iceberg, and how does it differ from traditional partitioning techniques? - How does Iceberg handle large-scale data versioning and rollback? 🚨 To learn more, Join my high quality, industry & modern tech stack based and practical project driven Data Engineering BootCAMP✌🏻👇 👉 Enroll Here - https://lnkd.in/dRgYCmWd 🚀 Dedicated Doubt Support & Placement Assistance 📲 Call/WhatsApp for any query (+91) 9893181542 Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar

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Q. Why Snowflake & Databricks moves ahead and did acquisitions in upstream & downstream, "Data Ingestion" & "AI Modeling/AI Ops" ? 💭 Embracing the evolution of the warehouse/lakehouse where storage, compute, and knowledge are independent yet scalable, adapting dynamically to the volume or changes in data. Q. Can the prime focus on scalability, elasticity, and performance lead to deep vendor lock-in, limiting options in their data maturity journey? Even with tight vendor lock-in, organizations must still push for rapid innovation and accelerate AI product development. But could this focus become a cost barrier that hampers the ability to see real ROI? #warehouse #lakehouse #modernwarehouse

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Hadoop Vs Spark Let's explore a few scenarios!! Scenario 1: Interviewer: We've discussed how Spark replaced MapReduce in many data processing workflows. Can you explain a scenario where Spark's in-memory processing model provides a significant performance advantage over Hadoop MapReduce? Candidate: Certainly. Consider a scenario where we need to perform iterative machine learning algorithms, such as training a large-scale model using gradient descent. In Hadoop MapReduce, each iteration involves reading the data from disk, processing it, and writing intermediate results back to disk, incurring significant overhead due to disk I/O operations. In contrast, Spark keeps the data in memory between iterations, eliminating the need for costly disk reads and writes. This allows Spark to perform iterative algorithms much faster than MapReduce, making it well-suited for machine learning tasks. Interviewer: That's a clear illustration of Spark's performance advantage in iterative workloads. Thank you for explaining. Scenario 2: Interviewer: Let's discuss the versatility of Spark compared to Hadoop MapReduce. Can you provide an example of a scenario where Spark's support for multiple processing modes enables users to address diverse analytical use cases? Candidate: Absolutely. Consider a scenario where an organization needs to build a real-time analytics dashboard to monitor key performance metrics in their business operations. In this scenario, Spark's support for streaming data processing with Spark Streaming allows the organization to ingest, process, and analyze streaming data in real-time. By integrating streaming analytics with batch processing and machine learning using the same Spark framework, the organization can build an end-to-end data pipeline to address both real-time and historical analytics use cases seamlessly. Interviewer: That's a compelling example of how Spark's versatility enables organizations to address a wide range of analytical use cases. Thank you for sharing. Scenario 3: Interviewer: Let's discuss Spark's programming model and its impact on developer productivity. Can you provide an example of how Spark's high-level APIs simplify the development of complex data processing workflows? Candidate: Certainly. Consider a scenario where we need to perform complex data transformations and aggregations on a large dataset. With Spark's DataFrame API, developers can express these transformations using high-level operations like filter, groupBy, and agg, which abstract away the underlying details of distributed processing. This allows developers to write concise and expressive code, reducing the complexity of data processing workflows and accelerating development. Additionally, Spark's support for multiple programming languages such as Scala, Python, and Java enables developers to work with their preferred language! Interviewer: That's an excellent demonstration. #bigdata #dataengineering

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