HBase as the NoSQL database of choice in the Hadoop ecosystem has already been proven itself in scale and in many mission critical workloads in hundreds of companies. Phoenix as the SQL layer on top of HBase, has been increasingly becoming the tool of choice as the perfect complementary for HBase. Phoenix is now being used more and more for super low latency querying and fast analytics across a large number of users in production deployments. In this talk, we will cover what makes Phoenix attractive among current and prospective HBase users, like SQL support, JDBC, data modeling, secondary indexing, UDFs, and also go over recent improvements like Query Server, ODBC drivers, ACID transactions, Spark integration, etc. We will conclude by looking into items in the pipeline and how Phoenix and HBase interacts with other engines like Hive and Spark.
How to build a streaming Lakehouse with Flink, Kafka, and HudiFlink Forward
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
This document discusses improvements to ORC support in Apache Spark 2.3. It describes previous issues with ORC performance and compatibility in Spark. The current approach in Spark 2.3 introduces a new native ORC file format that provides significantly better performance compared to the previous Hive ORC implementation. It allows configuring the ORC implementation and reader type. The document also demonstrates ORC usage in Spark and PySpark. Benchmark results show the native ORC reader provides up to 15x faster performance for scans and predicate pushdown. Future work items are discussed to further improve ORC support in Spark.
The document summarizes Apache Phoenix and HBase as an enterprise data warehouse solution. It discusses how Phoenix provides OLTP and analytics capabilities over HBase. It then covers various use cases where companies are using Phoenix and HBase, including for web analytics and time series data. Finally, it discusses optimizations that can be made to the schema design, queries, and writes in Phoenix to improve performance.
Traditionally database systems were optimized either for OLAP either for OLTP workloads. Such mainstream DBMSes like Postgres,MySQL,... are mostly used for OLTP, while Greenplum, Vertica, Clickhouse, SparkSQL,... are oriented on analytic queries. But right now many companies do not want to have two different data stores for OLAP/OLTP and need to perform analytic queries on most recent data. I want to discuss which features should be added to Postgres to efficiently handle HTAP workload.
The document summarizes Apache Phoenix and its past, present, and future as a SQL interface for HBase. It describes Phoenix's architecture and key features like secondary indexes, joins, aggregations, and transactions. Recent releases added functional indexes, the Phoenix Query Server, and initial transaction support. Future plans include improvements to local indexes, integration with Calcite and Hive, and adding JSON and other SQL features. The document aims to provide an overview of Phoenix's capabilities and roadmap for building a full-featured SQL layer over HBase.
Application Timeline Server - Past, Present and FutureVARUN SAXENA
How YARN Application timeline server evolved from Application History Server to Application Timeline Server v1 to ATSv2 or ATS Next gen, which is currently under development.
This slide was present at Hadoop Big Data Meetup at eBay, Bangalore, India.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
This document discusses tuning HBase and HDFS for performance and correctness. Some key recommendations include:
- Enable HDFS sync on close and sync behind writes for correctness on power failures.
- Tune HBase compaction settings like blockingStoreFiles and compactionThreshold based on whether the workload is read-heavy or write-heavy.
- Size RegionServer machines based on disk size, heap size, and number of cores to optimize for the workload.
- Set client and server RPC chunk sizes like hbase.client.write.buffer to 2MB to maximize network throughput.
- Configure various garbage collection settings in HBase like -Xmn512m and -XX:+UseCMSInit
Redis is an in-memory key-value store that is often used as a database, cache, and message broker. It supports various data structures like strings, hashes, lists, sets, and sorted sets. While data is stored in memory for fast access, Redis can also persist data to disk. It is widely used by companies like GitHub, Craigslist, and Engine Yard to power applications with high performance needs.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
This document discusses Druid in production at Fyber, a company that indexes 5 terabytes of data daily from various sources into Druid. It describes the hardware used, including 30 historical nodes and 2 broker nodes. Issues addressed include slow query times with many dimensions, some as lists, and data cleanup steps to reduce cardinality like replacing values. Segment sizing and partitioning are also discussed. Hardware, data ingestion, querying, and optimizations used to scale Druid for Fyber's analytics needs are covered in under 3 sentences.
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
Best Practices for ETL with Apache NiFi on Kubernetes - Albert Lewandowski, G...GetInData
Did you like it? Check out our E-book: Apache NiFi - A Complete Guide
https://ebook.getindata.com/apache-nifi-complete-guide
Apache NiFi is one of the most popular services for running ETL pipelines otherwise it’s not the youngest technology. During the talk, there are described all details about migrating pipelines from the old Hadoop platform to the Kubernetes, managing everything as the code, monitoring all corner cases of NiFi and making it a robust solution that is user-friendly even for non-programmers.
Author: Albert Lewandowski
Linkedin: https://www.linkedin.com/in/albert-lewandowski/
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Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
The document provides an introduction and overview of Apache NiFi and its architecture. It discusses how NiFi can be used to effectively manage and move data between different producers and consumers. It also summarizes key NiFi features like guaranteed delivery, data buffering, prioritization, and data provenance. Finally, it briefly outlines the NiFi architecture and components as well as opportunities for the future of the MiniFi project.
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
MongoDB is an open-source, document-oriented database that provides high performance and horizontal scalability. It uses a document-model where data is organized in flexible, JSON-like documents rather than rigidly defined rows and tables. Documents can contain multiple types of nested objects and arrays. MongoDB is best suited for applications that need to store large amounts of unstructured or semi-structured data and benefit from horizontal scalability and high performance.
This document provides an overview of Hive and its performance capabilities. It discusses Hive's SQL interface for querying large datasets stored in Hadoop, its architecture which compiles SQL queries into MapReduce jobs, and its support for SQL semantics and datatypes. The document also covers techniques for optimizing Hive performance, including data abstractions like partitions, buckets and skews. It describes different join strategies in Hive like shuffle joins, broadcast joins and sort-merge bucket joins and how they are implemented in MapReduce. The overall presentation aims to explain how Hive provides scalable SQL processing for big data.
Understanding and Improving Code GenerationDatabricks
Code generation is integral to Spark’s physical execution engine. When implemented, the Spark engine creates optimized bytecode at runtime improving performance when compared to interpreted execution. Spark has taken the next step with whole-stage codegen which collapses an entire query into a single function.
This talk with give and overview of exciting two releases for Apache HBase and Phoenix. HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the next evolution from the Apache HBase community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next biggest and most exciting milestone release because of Phoenix integration with Apache Calcite which ads lot of performance benefits with new query optimizer and helps to integrate with other data sources, especially those also based on calcite. It has lot of cool features such as Encoded columns, Kafka, Hive integration, improvements in secondary index rebuilding and many performance improvements.
This talk will give an overview of two exciting releases for Apache HBase 2.0 and Phoenix 5.0. HBase provides a NoSQL column store on Hadoop for random, real-time read/write workloads. Phoenix provides SQL on top of HBase. HBase 2.0 contains a large number of features that were a long time in development, including rewritten region assignment, performance improvements (RPC, rewritten write pipeline, etc), async clients and WAL, a C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next big Phoenix release because of its integration with HBase 2.0 and a lot of performance improvements in support of secondary Indexes. It has many important new features such as encoded columns, Kafka and Hive integration, and many other performance improvements. This session will also describe the uses cases that HBase and Phoenix are a good architectural fit for.
Speaker: Alan Gates, Co-Founder, Hortonworks
This talk will give an overview of two exciting releases for Apache HBase 2.0 and Phoenix 5.0. HBase provides a NoSQL column store on Hadoop for random, real-time read/write workloads. Phoenix provides SQL on top of HBase. HBase 2.0 contains a large number of features that were a long time in development, including rewritten region assignment, performance improvements (RPC, rewritten write pipeline, etc), async clients and WAL, a C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next big Phoenix release because of its integration with HBase 2.0 and a lot of performance improvements in support of secondary Indexes. It has many important new features such as encoded columns, Kafka and Hive integration, and many other performance improvements. This session will also describe the uses cases that HBase and Phoenix are a good architectural fit for.
Apache Phoenix and Apache HBase: An Enterprise Grade Data WarehouseJosh Elser
An overview of Apache Phoenix and Apache HBase from the angle of a traditional data warehousing solution. This talk focuses on where this open-source architect fits into the market outlines the features and integrations of the product, showing that it is a viable alternative to traditional data warehousing solutions.
HBase Read High Availability Using Timeline Consistent Region Replicasenissoz
This document summarizes a talk on implementing timeline consistency for HBase region replicas. It introduces the concept of region replicas, where each region has multiple copies hosted on different servers. The primary accepts writes, while secondary replicas are read-only. Reads from secondaries return possibly stale data. The talk outlines the implementation of region replicas in HBase, including updates to the master, region servers, and IPC. It discusses data replication approaches and next steps to implement write replication using the write-ahead log. The goal is to provide high availability for reads in HBase while tolerating single-server failures.
Data Governance in Apache Falcon - Hadoop Summit Brussels 2015 Seetharam Venkatesh
Apache Falcon is a data management platform that allows users to centrally manage data lifecycles across Hadoop clusters. It defines data entities like clusters, feeds, and processes to represent data pipelines. Falcon then automatically generates workflows to orchestrate the movement of data according to defined policies for replication, retention, and late data handling. It also provides data governance features like lineage tracing, auditing, and tagging. The latest version of Falcon includes new capabilities for disaster recovery mirroring and replication to cloud storage services.
Design Patterns For Real Time Streaming Data AnalyticsDataWorks Summit
This document provides an overview of design patterns for real-time streaming data analytics. It discusses architectural patterns like real-time streaming and lambda architecture. It also covers functional patterns like stream joins and top N trends. Additionally, it describes data management patterns such as external lookup, responsive shuffling, and handling out-of-sequence events. The presentation includes examples and benefits of these patterns for building scalable streaming applications.
The document provides an overview of new features in Apache Ambari 2.1, including rolling upgrades, alerts, metrics, an enhanced dashboard, smart configurations, views, Kerberos automation, and blueprints. Key highlights include the ability to perform rolling upgrades of Hadoop clusters without downtime by managing different software versions side-by-side, new alert types and a user interface for viewing and customizing alerts, integration of a metrics service for collecting and querying metrics from Hadoop services, customizable service dashboards with new widget types, smart configurations that provide recommended values and validate configurations based on cluster attributes and dependencies, and automated Kerberos configuration.
This presentation will give an overview of two exciting releases for Apache HBase and Phoenix. HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2018. It is the next evolution from the Apache HBase community after 1.0. HBase-2.0 contains a large number of features that are long time in development, some of which include rewritten region assignment , perf improvements (RPC, rewritten write pipeline, etc), async clients and WAL, C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next biggest release because of its integration with HBase 2.0 and lot of performance improvements in support of secondary Indexes. It has a lot of cool features such as encoded columns, Kafka, Hive integration, and many other performance improvements.
We have presented this at Data work summit 2018 in San Jose.
HBase Read High Availability Using Timeline-Consistent Region ReplicasHBaseCon
Speakers: Enis Soztutar and Devaraj Das (Hortonworks)
HBase has ACID semantics within a row that make it a perfect candidate for a lot of real-time serving workloads. However, single homing a region to a server implies some periods of unavailability for the regions after a server crash. Although the mean time to recovery has improved a lot recently, for some use cases, it is still preferable to do possibly stale reads while the region is recovering. In this talk, you will get an overview of our design and implementation of region replicas in HBase, which provide timeline-consistent reads even when the primary region is unavailable or busy.
Using Apache Hadoop and related technologies as a data warehouse has been an area of interest since the early days of Hadoop. In recent years Hive has made great strides towards enabling data warehousing by expanding its SQL coverage, adding transactions, and enabling sub-second queries with LLAP. But data warehousing requires more than a full powered SQL engine. Security, governance, data movement, workload management, monitoring, and user tools are required as well. These functions are being addressed by other Apache projects such as Ranger, Atlas, Falcon, Ambari, and Zeppelin. This talk will examine how these projects can be assembled to build a data warehousing solution. It will also discuss features and performance work going on in Hive and the other projects that will enable more data warehousing use cases. These include use cases like data ingestion using merge, support for OLAP cubing queries via Hive’s integration with Druid, expanded SQL coverage, replication of data between data warehouses, advanced access control options, data discovery, and user tools to manage, monitor, and query the warehouse.
The document discusses new features in Apache Hive 3 including the Data Analytics Studio, connectors to other data systems like Druid and Kafka, and SQL enhancements such as materialized views, constraints and defaults, and query result caching. It provides examples of how these new capabilities can optimize workloads, improve query performance, and enable more flexible data integration and analysis.
The document discusses recent releases and major new features of HBase 2.0 and Phoenix 5.0. HBase 2.0 focuses on off-heap memory usage to improve performance, as well as new features like async client, region assignment improvements, and backup/restore capabilities. Phoenix 5.0 includes API cleanup, improved join processing using cost-based optimizations, enhanced index handling including failure recovery, and integration with Apache Kafka.
HBase Read High Availabilty using Timeline Consistent Region ReplicasDataWorks Summit
The document describes HBase's new timeline consistency feature for high availability reads using region replicas. It discusses how region replicas work, including that the primary region accepts writes while secondary replicas only allow reads. It introduces the new timeline consistency option for reads that returns results from secondary replicas, allowing for highly available reads. The implementation details and future work are also outlined.
Data Con LA 2018 - Streaming and IoT by Pat AlwellData Con LA
Hortonworks DataFlow (HDF) is built with the vision of creating a platform that enables enterprises to build dataflow management and streaming analytics solutions that collect, curate, analyze and act on data in motion across the datacenter and cloud. Do you want to be able to provide a complete end-to-end streaming solution, from an IoT device all the way to a dashboard for your business users with no code? Come to this session to learn how this is now possible with HDF 3.1.
This document discusses strategies for achieving sub-second SQL query performance on Hadoop at scale. It describes two use cases: highly parallel batch reporting on a massive dataset, and online reporting with low latency requirements. For the latter use case, the document evaluates Hive LLAP and Phoenix, finding that Phoenix generally has lower latency, especially for queries with large result sets, through optimizations like skip scans, merging improvements, and table splitting. Tuning HBase and Phoenix configurations can further reduce latency.
Similar to Apache phoenix: Past, Present and Future of SQL over HBAse (20)
This document summarizes an upcoming presentation on HBase 2.0 and Phoenix 5.0. It discusses recent HBase releases and versioning, changes in HBase 2.0 behavior, and major new features like offheap caching, compacting memstores, and an async client. It also notes that HBase 2.0 is expected by the end of 2017 and provides guidance on testing alpha/beta releases. Phoenix 5.0 will add support for HBase 2.0 and improve its SQL parser, planner, and optimizer using Apache Calcite.
HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Existing users of HBase/Phoenix as well as operators managing HBase clusters will benefit the most where they can learn about the new release and the long list of features. We will also briefly cover earlier 1.x release lines and compatibility and upgrade paths for existing users and conclude by giving an outlook on the next level of initiatives for the project.
The document discusses best practices for operating and supporting Apache HBase. It outlines tools like the HBase UI and HBCK that can be used to debug issues. The top categories of issues covered are region server stability problems, read/write performance, and inconsistencies. SmartSense is introduced as a tool that can help detect configuration issues proactively.
- The document summarizes the state of Apache HBase, including recent releases, compatibility between versions, and new developments.
- Key releases include HBase 1.1, 1.2, and 1.3, which added features like async RPC client, scan improvements, and date-tiered compaction. HBase 2.0 is targeting compatibility improvements and major changes to data layout and assignment.
- New developments include date-tiered compaction for time series data, Spark integration, and ongoing work on async operations, replication 2.0, and reducing garbage collection overhead.
The document summarizes the HBase 1.0 release which introduces major new features and interfaces including a new client API, region replicas for high availability, online configuration changes, and semantic versioning. It describes goals of laying a stable foundation, stabilizing clusters and clients, and making versioning explicit. Compatibility with earlier versions is discussed and the new interfaces like ConnectionFactory, Connection, Table and BufferedMutator are introduced along with examples of using them.
The document proposes using MapReduce jobs to perform scans over HBase snapshots. Snapshots provide immutable data from HBase tables. The MapReduce jobs would bypass region servers and scan snapshot files directly for improved performance. An initial implementation called TableSnapshotInputFormat is described which restores snapshot data and runs scans in parallel across map tasks. The implementation addresses security and performance aspects. An API for client-side scanning of snapshots is also proposed to allow snapshot scans outside of MapReduce.
HBase and HDFS: Understanding FileSystem Usage in HBaseenissoz
This document discusses file system usage in HBase. It provides an overview of the three main file types in HBase: write-ahead logs (WALs), data files, and reference files. It describes durability semantics, IO fencing techniques for region server recovery, and how HBase leverages data locality through short circuit reads, checksums, and block placement hints. The document is intended help understand HBase's interactions with HDFS for tuning IO performance.
Software Engineering and Project Management - Introduction to Project ManagementPrakhyath Rai
Introduction to Project Management: Introduction, Project and Importance of Project Management, Contract Management, Activities Covered by Software Project Management, Plans, Methods and Methodologies, some ways of categorizing Software Projects, Stakeholders, Setting Objectives, Business Case, Project Success and Failure, Management and Management Control, Project Management life cycle, Traditional versus Modern Project Management Practices.
A brief introduction to quadcopter (drone) working. It provides an overview of flight stability, dynamics, general control system block diagram, and the electronic hardware.
How to Manage Internal Notes in Odoo 17 POSCeline George
In this slide, we'll explore how to leverage internal notes within Odoo 17 POS to enhance communication and streamline operations. Internal notes provide a platform for staff to exchange crucial information regarding orders, customers, or specific tasks, all while remaining invisible to the customer. This fosters improved collaboration and ensures everyone on the team is on the same page.
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
20CDE09- INFORMATION DESIGN
UNIT I INCEPTION OF INFORMATION DESIGN
Introduction and Definition
History of Information Design
Need of Information Design
Types of Information Design
Identifying audience
Defining the audience and their needs
Inclusivity and Visual impairment
Case study.
Understanding Cybersecurity Breaches: Causes, Consequences, and PreventionBert Blevins
Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks.
Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness.
The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.
Response & Safe AI at Summer School of AI at IIITHIIIT Hyderabad
Talk covering Guardrails , Jailbreak, What is an alignment problem? RLHF, EU AI Act, Machine & Graph unlearning, Bias, Inconsistency, Probing, Interpretability, Bias
Literature Reivew of Student Center DesignPriyankaKarn3
It was back in 2020, during the COVID-19 lockdown Period when we were introduced to an Online learning system and had to carry out our Design studio work. The students of the Institute of Engineering, Purwanchal Campus, Dharan did the literature study and research. The team was of Prakash Roka Magar, Priyanka Karn (me), Riwaz Upreti, Sandip Seth, and Ujjwal Dev from the Department of Architecture. It was just a scratch draft made out of the initial phase of study just after the topic was introduced. It was one of the best teams I had worked with, shared lots of memories, and learned a lot.