Ritabrata Saha

In #PostgreSQL, global indexes use a shared-nothing architecture to distribute index data across multiple nodes or shards, leveraging table partitioning. PostgreSQL enables parallel processing and enhances performance for large-scale workloads by indexing partitioned tables based on a partition key. #PostgreSQLPerformance @MinervaDB #dba https://zurl.co/a4zF

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As our applications evolve, so must our approach to data storage. Let's examine key factors in selecting the right database for advanced use cases. Also, Join me for an upcoming Free webinar - RSVP here : https://brij.guru/ai 𝗪𝗲'𝗹𝗹 𝗰𝗼𝘃𝗲𝗿: • Fundamentals of database branching • Practical implementation strategies • How to leverage this technique for AI applications 1. Object-Oriented DBs: Consider for complex object models where relational mapping becomes inefficient. Useful in CAD/CAM systems or scientific applications. 2. Time-Series DBs: Crucial for IoT and monitoring at scale. Look into options like InfluxDB or TimescaleDB for high-write, time-indexed data. 3. Graph DBs: Essential for relationship-heavy data. Neo4j and Amazon Neptune excel in scenarios like fraud detection or knowledge graphs. 4. Document DBs: MongoDB and Couchbase offer flexibility for rapidly changing schemas and horizontal scaling. Ideal for content management and real-time big data. 5. Columnar DBs: Vertica and ClickHouse provide superior performance for analytical workloads, especially in data warehousing scenarios. 6. In-Memory DBs: Redis and Memcached shine in caching layers and real-time bidding systems where sub-millisecond response times are critical. 7. NoSQL DBs: Beyond basic key-value stores, consider specialized options like Cassandra for write-heavy, distributed systems. 8. Relational DBs: Postgres and MySQL continue to evolve, now offering JSON support and improved scalability. Still the go-to for transactional systems. 9. Vector DBs: Milvus and Pinecone are pushing boundaries in AI applications, offering efficient similarity search for large-scale machine learning models. Key considerations: - Data model complexity - Read/write patterns - Scalability requirements - Consistency vs. availability tradeoffs - Operational costs and expertise Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

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Excited to share that Vijay K. and I collaborated on a guide for Active-Active Replication on Amazon RDS for MySQL! This comprehensive resource is now available on AWS Solutions https://lnkd.in/d2nH2NH3 #aws #amazonrds #mysql #highavailability #disasterrecovery #rds #mysql

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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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#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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Fast-growing undo logs in #MySQL can degrade performance by increasing disk and memory usage, causing contention, slowing checkpointing, and extending rollback times. Monitoring and adjusting settings like innodb_undo_logs_size and undo_log_size can help manage undo log growth. #MySQLPerformance @MinervaDB #dba https://zurl.co/5Bmh

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It gives us great pleasure to announce that Apache Spark in BigQuery is now officially available in Private Preview. This feature expands BigQuery’s processing capabilities by offering Apache Spark as an OSS processing engine under a unified control plane (admin, monitoring, billing, security, governance). It is the first step in delivering on the Unified Data Platform vision and was a key announcement at Next ‘24 #googlecloud #bigquery #spark

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At Confluent's Kafka Summit in Bangalore, Shaun Clowes, Chief Product Officer, and Jay Kreps, Co-Founder and CEO, discussed Confluent's strategic approach to enhancing enterprise data infrastructure amidst the AI revolution. "Every time you have a new data need, what do you do? You connect the destination point-to-point to all the different sources needed. Over time, your data infrastructure turns into a giant, tangled mess of interconnections," shared Shaun Clowes. “Kafka often serves as the data feed into this area. However, when considering the various tools available, they generally run on not just streams, but largely on extensive tables of data. This is the essence of a data warehouse or data lake," Jay Kreps explained. #BigData #DataManagement #DigitalTransformation

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Take that Azure Cosmos DB powered app to prod with the help from Sandeep and Saranya! Watch the three part video series: "Mastering #AzureCosmosDB: A Comprehensive Guide from Prototype to Production." https://lnkd.in/eEMt5TFG

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Integrating Microsoft Fabric Data Pipelines with YugabyteDB using incremental data loading is an efficient and scalable solution for managing large datasets. This new blog from Yugabyte expert Bala Seetharaman explores how you can easily ingest data from YugabyteDB and process and store it in Microsoft Fabric Lakehouse using Microsoft Fabric’s Data Pipeline.💡 The result is seamless data flow, centralized storage, and robust analytics capabilities, enabling effective data-driven decision-making.🚀 Find out more! ⬇️ https://hubs.la/Q02PYGgw0 #datalake #distributedsql #cloudnative #dataloading #YugabyteDB

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SPARK code to find out the product, which generates the maximum revenue for a commercial store. #SCALA #SPARK #BIGDATA Karthik K. INPUT: PRODUCT_ID,QUANTITY_SOLD,PRICE 101,10,150 101,15,150 102,5,50 102,10,50 103,3,100 103,10,100 QUERY: def main(args: Array[String]):Unit = { val ss = SparkSession.builder.appName("two_dfS").master("local[*]").getOrCreate() val df_1 = ss.read.format("csv").option("header",true). option("path","C:/Users/hp/PRODUCT_ID.txt"). schema("PRODUCT_ID Int, QUANTITY_SOLD Int, PRICE Int").load() val df_2 = df_1.withColumn("REVENUE",col("QUANTITY_SOLD")*col("PRICE")) val df_3 =df_2.groupBy(col("PRODUCT_ID")). agg(sum(col("QUANTITY_SOLD")).as("TOTAL_QUANTITY_SOLD"), sum(col("REVENUE")).as("TOTAL_REVENUE")) df_3.show() val wind = Window.orderBy(col("TOTAL_REVENUE").desc) val df_4 = df_3.select(col("PRODUCT_ID"),col("TOTAL_REVENUE"),row_number().over(wind).as("ROW_NUMBER")) df_4.filter(col("ROW_NUMBER")<=1).show() OUTPUT:

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Snowflake summit 2024 highlights doc for your reference. #snowflake #summit #updates

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Customers are using the Apache Iceberg format because they can read and write data concurrently using different frameworks. Iceberg format also allows for time travel and rollback so you can run SQL queries on a point-in-time snapshot of your data, or rollback data to a previously known good version. Now, you can stream real-time data into Apache Iceberg tables in Amazon S3 using Amazon Data Firehose. https://lnkd.in/g8g3FUah #Data #StreamingData #DataStreaming #Firehose #S3 #AWS #ApacheIceberg #Iceberg

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PSA: enforce secure MFA (everywhere) and follow Bob Lord’s advice (https://lnkd.in/ggWEwQC5) https://lnkd.in/gJmQRJy6 Snowflake Mandiant (part of Google Cloud) "Mandiant's investigation has not found any evidence to suggest that unauthorized access to Snowflake customer accounts stemmed from a breach of Snowflake's enterprise environment. Instead, every incident Mandiant responded to associated with this campaign was traced back to compromised customer credentials...To date, Mandiant and Snowflake have notified approximately 165 potentially exposed organizations. Snowflake’s Customer...The broad impact of this campaign underscores the urgent need for credential monitoring, the universal enforcement of MFA and secure authentication, limiting traffic to trusted locations for crown jewels, and alerting on abnormal access attempts. Mandiant identified that the majority of the credentials used by UNC5537 were available from historical infostealer infections, some of which dated as far back as 2020. The threat campaign conducted by UNC5537 has resulted in numerous successful compromises due to three primary factors: 🚨 The impacted accounts were not configured with multi-factor authentication enabled, meaning successful authentication only required a valid username and password. 🚨 Credentials identified in infostealer malware output were still valid, in some cases years after they were stolen, and had not been rotated or updated. 🚨 The impacted Snowflake customer instances did not have network allow lists in place to only allow access from trusted locations. In several Snowflake related investigations, Mandiant observed that the initial compromise of infostealer malware occurred on contractor systems that were also used for personal activities, including gaming and downloads of pirated software. Contractors that customers engage to assist with their use of Snowflake may utilize personal and/or non-monitored laptops that exacerbate this initial entry vector. These devices, often used to access the systems of multiple organizations, present a significant risk. If compromised by infostealer malware, a single contractor's laptop can facilitate threat actor access across multiple organizations, often with IT and administrator-level privileges...UNC5537 primarily used Mullvad VPN or Private Internet Access (PIA) VPN IP addresses to access victim Snowflake instances."

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Interesting read on Databricks Fundamentals. Delta Lake and Photon are two pivotal technologies within the Databricks ecosystem, revolutionizing the way organizations manage and analyze their data. 1. Delta Lake - Open-source storage layer that brings ACID Atomicity, Consistency, Isolation, Durability) transactions to Apache Spark and big data workloads. It provides reliability, performance, and data management capabilities for big data analytics. Other Key Features in Delta Lake - Schema enforcement prevents users from accidentally polluting their tables with mistakes or garbage data. - Schema Evolution - Delta Lake supports schema evolution, allowing schemas to evolve over time while maintaining backward compatibility. This enables flexibility in data ingestion and schema management, reducing development overhead and improving productivity. 2. Photon: Photon is a distributed query engine optimized for Databricks Delta Lake. It accelerates query performance and improves resource utilization for analytics workloads by leveraging advanced query optimization techniques and in-memory processing. https://lnkd.in/gpxTGEDm

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Download a free Redshift Data Warehouse case study to deepen your understanding of this advanced solution. https://lnkd.in/gum4KiV9 Embark on the journey from On-Premises Netezza to Redshift Cloud Data Warehouse with our comprehensive carousel. Explore the key stages of this migration process: Assessment of Existing Infrastructure, Planning and Strategy, Data Migration, Optimization for Redshift, Testing and Validation, and Training and Adoption. Each step is crucial in ensuring a smooth transition to the cloud, enhancing scalability, agility, and efficiency. Join us as we delve into the intricacies of migrating to Redshift and unlock the potential of cloud data warehousing for your business. #migration #cloudmigration #datamigration #platformmigration #ERPmigration #CRMmigration #datawarehouse #businessintelligence #datascience #computer #outsourcing #services #dataanalytic #complereinfosystem

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