Sumit Mittal

#ApacheHudi is designed as a specialized storage layer, offering advanced features like indexing, change streams, incremental queries, and log merges to power incremental data processing. In this #InfoQ video, Saketh Chintapalli & Bhavani Sudha Saktheeswaran share real-world experiences gained from operating #Uber’s critical data warehousing pipelines on the new incremental model leveraging Apache Hudi, along with results from the OSS community. 👀 Watch now: https://bit.ly/4dTSRe7 #transcript included #DataEngineering #BigData #DataWarehousing #IncrementalProcessing

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Understanding Narrow and Wide Transformations in Apache Spark In Apache Spark, transformations play a critical role in defining how data is processed across distributed clusters. These transformations can be broadly classified into narrow transformations and wide transformations, each influencing the efficiency and data flow within Spark applications. 1. Narrow Transformations: A narrow transformation is one in which each output partition depends only on a single input partition. Since the data required to generate an output partition resides in the same partition as the input data, Spark can process these transformations without requiring data to move across nodes. This minimizes network traffic and improves performance. Examples of Narrow Transformations: • map(): Applies a function to each element of the RDD/DataFrame without requiring data exchange between partitions. • filter(): Selects elements based on a condition, retaining them within the same partition. • flatMap(): Similar to map(), but can generate multiple elements from a single input element, still preserving the partition boundaries. 2. Wide Transformations: Wide transformations involve operations where the output partition depends on data from multiple input partitions, often triggering a shuffle—a costly operation where data is exchanged across the cluster. These transformations require coordination between nodes, leading to network I/O, which can impact performance. Examples of Wide Transformations: • groupByKey(): Groups data by key, causing a shuffle as all values associated with the same key need to be aggregated across partitions. • reduceByKey(): Similar to groupByKey(), but applies a reducing function during the shuffle phase to optimize data movement. • join(): Combines data from two datasets based on a key, triggering a shuffle to align matching keys across partitions. Key Difference: Data Shuffling The fundamental difference between narrow and wide transformations lies in data shuffling. In narrow transformations, Spark processes data locally without any network overhead. However, in wide transformations, the data must be reshuffled across partitions, leading to increased computational and network costs. For example, a map() operation applies functions independently within partitions, while a reduceByKey() requires the data to be shuffled so that values for the same key are processed together. Follow me(Raghu Raman A V) for more articles on Big Data, Data Engineering, Azure and cloud ! #BigData #ApacheSpark #DataEngineering

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Snowflake Optimization Tip of the Day #5 ➡ Use incremental models in dbt There is no simpler way to reduce Snowflake cost than to process less data. If you are using dbt, try to write every model incrementally. Instead of reprocessing all data every time, incremental models only process new or changed data since the last run. This is achieved primarily by using date stamps or other markers to identify what data has been added or modified since the last run. Full-refreshes are very expensive and should be avoided if possible.

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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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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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Hadoop Vs Spark Interview Question!! Interviewer: Let's discuss the evolution of the Hadoop ecosystem and the rise of Apache Spark. One significant change that occurred was the replacement of Hadoop MapReduce with Spark in many data processing workflows. Can you explain why this transition happened? Candidate: Certainly! The transition from Hadoop MapReduce to Apache Spark can be attributed to several factors. One key reason is Spark's superior performance compared to MapReduce. Spark's in-memory processing model allows it to execute tasks much faster by minimizing data shuffling and disk I/O, leading to significant speed improvements for data processing tasks. Interviewer: That's an important point. Can you elaborate on how Spark's in-memory processing differs from Hadoop MapReduce's disk-based processing? Candidate: Of course! In Hadoop MapReduce, intermediate data between map and reduce tasks is typically written to disk, which can incur overhead due to disk I/O operations. In contrast, Spark keeps intermediate data in-memory whenever possible, reducing the need for disk reads and writes. This in-memory processing model enables Spark to achieve faster processing speeds, especially for iterative algorithms and interactive analytics workloads. Interviewer: I see. So, apart from performance, are there any other reasons why Spark replaced MapReduce in many data processing workflows? Candidate: Absolutely. Another factor is Spark's versatility. While Hadoop MapReduce is primarily designed for batch processing, Spark supports a wide range of processing workloads, including batch processing, interactive querying, streaming data processing, and machine learning. This versatility makes Spark a more attractive choice for organizations looking to address diverse analytical use cases within a single framework. Interviewer: That's a compelling advantage. How did Spark's programming model contribute to its popularity? Candidate: Spark offers a more developer-friendly programming model compared to Hadoop MapReduce. With its rich set of high-level APIs in languages like Scala, Python, and Java, Spark simplifies the development of complex data processing workflows. Additionally, Spark's interactive shell (Spark REPL) facilitates rapid prototyping and experimentation, enhancing developer productivity. Interviewer: Thank you for the insightful explanation. It's clear that Spark's performance, versatility, and ease of use have made it a preferred choice for many organizations seeking to modernize their data processing infrastructure. Candidate: My pleasure! #DataEngineering #PySpark #ETLProject #pyspark #python #azuredataengineer #databricks #awscloud #googlecloud #dataengineer #dataengineerjobs #dataanalysis #dataanalytics #glue #azuredatabricks #azuredatafactory #synapse #sql #sqlserver #powerbi #database #etl #etldeveloper #Python #Pandas #BeginnertoAdvance #Dataframe #Sorting #Joins #Grouping #RealTimeScenarios #sql #awscloud #powerbi #databricks

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Enter the ultimate face-off: Apache Spark vs. Apache Hadoop. These two powerful tools serve distinct purposes.                Know more ......          👇👇👇👇👇👇👇👇👇👇👇👇👇 https://lnkd.in/gYAFCM7b #Apachespark #ApacheHadoop #DataProcessing #RealTimeAnalytics #BatchProcessing #BigDataTools #DataAnalytics #CloudComputing #DataStorageSolutions #MachineLearning #DataArchitecture

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Snowflake Interview Question!! Q1- Can you explain how Snowflake’s architecture, with its separation of storage and compute, automatic micro-partitioning, and result caching, helps in optimizing query performance? Snowflake’s architecture optimizes query performance through several key features: 1.Separation of Storage and Compute: This allows compute resources to be scaled independently of storage. It enables allocating more compute power during peak query times without impacting data storage costs. Concurrent workloads are supported by allowing multiple virtual warehouses to access the same data simultaneously, ensuring high performance and resource isolation. 2.Automatic Micro-Partitioning: Snowflake stores data in small, contiguous units known as micro-partitions. These are automatically managed and optimized by Snowflake, including creating, reorganizing, and optimizing the partitions while ingesting and querying data. Extensive metadata for each micro-partition efficiently prunes irrelevant data during query execution, reducing the scanned data volume and enhancing query performance. 3.Result Caching: Snowflake caches results of previously executed queries. When the same query is rerun, Snowflake retrieves results from the cache instead of rerunning the query, significantly reducing execution time and resource usage. This automatic result caching accelerates repeated queries and provides cost savings by minimizing the need for additional compute resources. Q2- How clustering keys can optimize data retrieval and reduce scan times.  Clustering keys in Snowflake optimize data retrieval and reduce scan times by organizing data within micro-partitions based on specified columns. This helps improve query performance, especially for large datasets. Benefits of Clustering Keys 1.Reduced Scan Times: By clustering data, Snowflake can skip irrelevant micro-partitions during query execution, leading to faster access times. 2.Efficient Filtering: Clustering helps the engine locate relevant rows quickly when queries filter on clustered columns, minimizing the amount of data scanned. 3.Data Pruning: Snowflake uses clustering keys to prune entire partitions that do not meet query criteria, significantly improving query performance. #snowflake #bigdata #dataengineer #pyspark

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🗓 Snowflake Optimization - Summary of the week 1. Prune Partitions Snowflake uses the concept of micropartitions. Apply filters to prune data. 2. Enable Automatic Clustering Automatic clustering can be useful for certain tables that are heavily queried on certain fields. It does cost credits, so you might need to a cost-benefit analysis 3. Reduce Idle time Set AUTO_SUSPEND = 60. 4. Consolidate warehouses Consolidate similar warehouses into a single multicluster warehouse. 5. Use incremental models Write incremental models in dbt. Incremental models only process the new incoming data based on a field, generally date.

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https://zurl.co/KZoz How do Bloom Indexes work in PostgreSQL? #PostgreSQL #PostgreSQLDBA #PostgreSQLTips #SQL #SQLTips #DBA #OpenSource #Cloud #DevOps

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Open-Source Database Migration Tools Considering migrating a database to an open-source platform like PostgreSQL, MySQL, or MariaDB, there are multiple tools and strategies available to facilitate the process. 1. **pgAdmin (for PostgreSQL):** pgAdmin is a widely-used open-source tool for managing and migrating PostgreSQL databases. It allows for data import/export, SQL script generation, and schema comparisons, enabling seamless migrations from SQL Server to PostgreSQL. 2. **MySQL Workbench (for MySQL):** MySQL Workbench is an integrated tool that supports database migrations, schema design, and database management for MySQL. Its migration wizard simplifies the process, supporting migrations from various databases to MySQL. 3. **pgLoader (for PostgreSQL):** pgLoader is a command-line tool designed for migrating data to PostgreSQL from databases like MySQL, SQLite, and SQL Server. It automates schema and data transfer, streamlining the migration process. 4. **Flyway:** Flyway is an open-source migration tool compatible with multiple databases, including PostgreSQL, MySQL, and MariaDB. By utilizing versioned SQL scripts, it eases database transitions efficiently. 5. **DBConvert/DBSync:** These tools facilitate data migration between different database systems, such as transferring data from SQL Server to MySQL or PostgreSQL. While DBConvert offers advanced features for complex migrations, it is a paid tool. 6. **Open Source Database Migration Toolkit (by Ispirer):** Ispirer Migration Toolkit is an open-source solution supporting database migrations from SQL Server to PostgreSQL, MySQL, and more. It automates many aspects of the migration process, offering customization options. 7. **DBeaver:** DBeaver is an open-source database management tool compatible with various databases, including PostgreSQL and MySQL. It provides functionalities for data migration, schema comparison, and data synchronization across different systems.

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https://zurl.co/OBRt Understanding Index Selection Mechanics in PostgreSQL #PostgreSQL #DBA #PostgreSQLPerformance #SQLTips #PostgreSQLConsulting

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InnoDB Internals from Mayank Prasad. He covered two major features in MySQL 8.0 🔵 Dynamic redo logging (WAL) 🔵 Instant Column Addition #mysql #innodb #mysqldba #reliability #sql #performance

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Someone rightly said : Snowflake is for rich, low to no-tech departments like Finance. Databricks is for data engineers. In the end, both are successful, multi-billion dollar companies. Choice in the end lies in defining a clear customer segment. #snowflake #databricks #dataengineering #customersegment

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https://zurl.co/3d0N Missing Statistics on PostgreSQL Query Performance #PostgreSQL #DBA #PostgreSQLPerformance #SQLTips #PostgreSQLConsulting

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Databricks Interview Question Part 1 You have an AWS Databricks job processing a large dataset stored in S3 that is running slower than expected. What are the possible reasons, and how would you troubleshoot and optimize the job? Ans:  1. Check for Data Skew/Partitioning   - Problem: Data skew happens when some partitions have much larger data than others, causing imbalance in how the data is distributed across the cluster. This leads to some executors working harder and taking longer to process their partitions, resulting in slower jobs.   - Solution: Analyze the partitioning of your data in S3. Use Spark’s `explain()` to inspect the physical plan and look for imbalanced data distribution.     - Repartition or coalesce the data evenly to ensure all partitions have a similar amount of data.    df = df.repartition(100, 'Key')    - Broadcast joins can help if one of the datasets is significantly smaller, which reduces shuffling.  2. Optimize Data Read/Write Operations   - Problem: Slow I/O between S3 and Databricks can bottleneck performance, especially if reading large files inefficiently.   - Solution:    - Columnar Storage: Ensure that the data in S3 is stored in a columnar format like Parquet or ORC. These formats allow you to read only the necessary columns, reducing data transfer and speeding up read times.     df = spark.read.parquet('s3://path/to/parquet_files')    - Predicate Pushdown: Ensure that you're filtering data as early as possible using predicate pushdown. If the job is reading too much irrelevant data, filter at the source.     ```python     df = spark.read.parquet('s3://path').filter(col('date') >= '2024-01-01')     ```  3. Tune Cluster Configuration   - Problem: Insufficient or improperly configured cluster resources (such as CPU, memory, or disk I/O) can cause slow performance.   - Solution:    - Auto-scaling: Enable auto-scaling to dynamically add or remove nodes based on the workload’s needs, especially if the workload is variable. AWS Databricks supports cluster autoscaling to scale up during high loads and scale down during idle times.    - Memory/CPU: Ensure that your worker nodes have enough memory and vCPU to handle the job. For example, increasing node types to larger instances or using spot instances can help optimize cost while improving performance.  4. Investigate Shuffling and Network Latency   - Problem: Excessive shuffling can slow down Spark jobs. Shuffling happens during operations like `groupBy`, `join`, or `orderBy`, which require data to be exchanged between executors.   - Solution:    - Avoid wide transformations unless absolutely necessary. Where possible, use map-side operations to minimize shuffling.    - Optimize joins by using broadcast joins when one dataset is small enough to be broadcast to all nodes.    - Enable adaptive query execution (AQE) in Spark 3.x to automatically optimize shuffles, joins, and partitioning at runtime.       spark.conf.set("spark.sql.adaptive.enabled", "true")

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An algorithm that helps DynamoDB avoid timeouts is called "request hedging". This short blog explains the algorithm: https://lnkd.in/dkWbfYCj You can find the previous two parts here: Part I - Request Collapsing: https://lnkd.in/dk9ytii7 Part II - Cron job scheduling: https://lnkd.in/d8kJGXt6 Let me know your thoughts and suggestions in the comments. Cheers!

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