Aakash Pradeep
Principal Software Engineer at Twilio Inc.
Principal Software Engineer at Twilio Inc.
Hadoop, Data Lake and high volume ingestion
Twilio Inc.
https://www.linkedin.com/company/twilio-inc-
Principal Software Engineer
Leading the Storage and Compute platform.
https://media-exp1.licdn.com/dms/image/C560BAQF5xT8PG6SGQQ/company-logo_100_100/0/1646709079647?e=1655942400&v=beta&t=p0Sqi5dWAfmJrMM2xjW2_iZcM22MF4DRpmH0TM6kdvo
Salesforce
https://www.linkedin.com/company/salesforce
Principal Member Of Technical Staff
Worked with Data Lake and Data warehouse. lead the ingestion pipeline to ingest 500+ billion message a day and managed 50+ in-premise Yarn Clusters to provide MR based multi-tenant compute as service.
San Francisco, California
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Salesforce
https://www.linkedin.com/company/salesforce
Lead Member of Technical Staff
Dealing with high volume low latency ingestion pipeline.
San Francisco Bay Area
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Salesforce
https://www.linkedin.com/company/salesforce
Senior Member of Technical Staff
Designed and developed a high volume and low latency data ingestion pipeline. This ingestion pipeline is based on new data structure called Concurrent Bounded Buffer and a Consumer Executor Service, which flushes the buffer based on size and time and consumes them in parallel fashion. This ingestion system is horizontally and vertically scalable. Currently, this ingestion system is used to ingest 100+ million events/hour in a shared and multi tenant set up with micro second latency.
San Francisco Bay Area
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Salesforce
https://www.linkedin.com/company/salesforce
Member of Technical Staff, Software Development
■	Designed and developed a system to parse structured log file using MapReduce and produce tenant specific file for each day. Besides parsing log file, these MapReduce job also filter log lines based on date, log record type and log record fields which do not follow a configured pattern and also sort tenant specific files based on timestamp and log record type. These log record are based on format which may change with different application version. This MapReduce based system is metadata- driven in order to make it version agnostic.
■	Part of the team that developed event-based system, which involves capturing, massaging and saving events to HBase. Currently, it captures events that are used for Identity forensic. It also has a component that is responsible to delete events that are older than the configured time.
San Francisco Bay Area
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Temple University
https://www.linkedin.com/company/templeuniversity
Mobile Web App Developer
Mobile web app developer for Fox School of Business.
Philadelphia
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Temple University
https://www.linkedin.com/company/templeuniversity
Student
I completed MS in Computer science (2011 - 2012) from Temple University.
Philadelphia
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salesforce.com
https://www.linkedin.com/company/salesforce
Summer Intern
Designed and developed a DELV tool, Data Extraction, loading and verification tool. It identifies foreign key dependencies even cyclic dependencies and resolves these dependencies in the best possible way while extracting and loading the data. APIs are designed in a generic way so that it can be extended to read data from any DB and write data to a different or same DB. Current version includes implementation for MySQL.
San Mateo, California
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Yahoo!
https://www.linkedin.com/company/yahoo
Senior Quality Engineer
Framework development,Automation Library development, White Box Engineering, Tested REST based Web application, Tested platform for Distributed processing of feeds(high volume less frequent and low volume high frequency) and tested Properties (e.g. shopping, real estate, finance) workflow for this platform
Bangalore, India
https://media-exp1.licdn.com/dms/image/C560BAQE1H3S3iDNY6Q/company-logo_100_100/0/1569251131340?e=1655942400&v=beta&t=FEGSgWZVA1d-mgBF14IpfWGXDG2IeSKrqj_qBoMH998
Yahoo! R&D
Intern
Designed and developed a framework in Ruby to automate the front-end testing of the web application using open
     source tool WATIR.
Computer Science
Master of Science
Temple University
https://www.linkedin.com/school/templeuniversity/
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Computer Application
Master of Computer Application
Vellore Institute of Technology
https://www.linkedin.com/school/vellore-institute-of-technology/
Gold Medalist,
awarded Merit Scholarship  for year 2005-06,2006-07,2007-08, 
Endowment Award 2007-08, 
Ranked among top 15 in IBM Great Mind Challenge 2006 - a National Level Software Development Program
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Computer Application
Bachelor in Computer Application
Indira Gandhi National Open University
https://www.linkedin.com/school/indira-gandhi-national-open-university/
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IEEE
Member
ACM
Member
AnkaCom: A Development and Experiment for Extreme Scale Computing
IEEE
Extreme scale computing has no implied scaling limit. The impossibility of implementing reliable communication between crashing hosts prohibits explicit-communication primitives in extreme scale applications. However, the alternatives are not well investigated. This paper reports the development and experimentation of a prototype implicit communication system called AnkaCom using a Statistic Multiplexed Computing (SMC) framework. The key concept of the SMC framework is total decoupling between application logical entities and the physical resources, such as processors, networks, databases and storages. The separation allows the integration of a SMC control layer that can automatically deliver mission critical services leveraging the maximal capabilities of the unreliable resources at runtime. The SMC control layer operates according to the principles of HDLC protocols that have been proven capable of delivering exactly once reliable services using unreliable lower level services. This paper reports the development and computational experiments of AnkaCom - an experimental SMC parallel processing system. AnkaCom is a Java implementation of a distributed tuple switch network (TSN) with in-memory replication capabilities. We study computational results of AnkaCom application against "bare metal" C/MPI application using a textbook dense matrix multiplication example. We show that granularity optimized AnkaCom application can consistently out-perform C/MPI application in a dedicated cluster of 12-120 cores. In-memory real time data replication effects are also reported to show marginal elapsed time changes with double and triple runtime tuple replicas.
http://ieeexplore.ieee.org/document/7363344/
Program Scalability Analysis for HPC Cloud: Applying Amdahl's Law to NAS Benchmarks
IEEE
The availability of high performance computing (HPC) clouds requires scalability analysis of parallel programs for multiple different environments in order to maximize the promised economic benefits. Unlike traditional HPC application performance studies that aim to predict performances of like-kind processors, this paper reports an instrumentation assisted complexity analysis method based on Amdahl's Law framework for program scalability analysis for different HPC environments. We show that program instrumentation helps Gustafson's scaled speedup formulation to quantify the elusive quality in Amdahl's Law. We report that without separating communication time from computing, prediction results are not trustworthy. We demonstrate a methodology that can transform asymptotic complexity models to timing models in order to separate communication time and to identify the optimal degree of parallelism. A traditional HPC cluster and a private HPC cloud are used to validate the proposed methodology by showing the feasibility of optimal parallel processing and by scalability analysis of five NAS benchmarks. Our results show that either cloud or cluster can be effectively exploited if the application can adapt to changing processing conditions dynamically. As we dig deeper into the performance analysis myths, “scalability limit” seems to mean less than its common interpretation but more on the inadequacy our programming habits and architecture support.
https://ieeexplore.ieee.org/document/6495929
P2PHDFS: AN IMPLEMENTATION OF STATISTIC MULTIPLEXED COMPUTING ARCHITECTURE IN HADOOP FILE SYSTEM
Temple University
he Peer to Peer Hadoop Distributed File System (P2PHDFS) is designed to store and process extremely large-scale data sets reliably. This is a first attempt implementation of the Statistic Multiplexed Computing Architecture concept proposed by Dr. Shi for the existing Hadoop File System (HDFS) to eliminate all single point failures. Unlike HDFS, in P2PHDFS every node is designed to be equal and behaves as a file system server as well as slave, which enable it to attain higher performance and higher reliability at the same time as the infrastructure up scales. Due to the data intensive nature, a full implementation of P2PHDFS must address CAP Theorem challenges. This MS project is only intended as the ground breaking point using only sequential replication at this time.
https://scholarshare.temple.edu/handle/20.500.12613/2184
Scheduling capacity in a data-processing cluster to an application queue by repurposing monitoring-based capacity of a delegator queue for job execution in the application queue
United States
US10725829B2
US10725829B2
https://patents.google.com/patent/US10725829B2/en?inventor=Aakash+Pradeep&page=1
Reliable generation of a unique key in a distributed system
United States
A database system may determine that a database is unavailable to generate an identifier for an event or that events may not be written to the database. As a result, the database system may write the event to a low-latency, supplementary file system. The database system may determine that the database is available at a later time, and read the event from the supplementary file system, store the event in a memory component, and then delete the event from the supplementary file system. The database system may then access the database to generate an identifier for the event, write the event to the database, and then delete the event from the memory component.
US10678757B2
US10678757B2
https://patents.google.com/patent/US10678757B2/
Adding a partition to an existing table stored on a distributed file system
United States
US10642803B2
US10642803B2
https://patents.google.com/patent/US10642803B2
Derived fields in log file processing
United States
Disclosed are some examples of database systems, methods, and computer program products for processing log files. In some implementations, a server of a database system accesses a metadata file indicating algorithms that can be applied to data of log files. The server generates customer-facing log files using the log file and metadata file. The customer-facing log files include new data derived from using the algorithms and the data of the log files.
US20170075922A1
US20170075922A1
https://patents.google.com/patent/US20170075922A1
Memory efficient policy-based file deletion system
United States
Disclosed are some implementations of systems, apparatus, methods and computer program products for facilitating policy-based file deletion. Policy-based file deletion is implemented via a tiered system that includes a master computing system and a plurality of slave computing systems. The master computing system distributes policies among the slave computing systems, which each applies assigned policies to cause deletion of files that satisfy those policies.
US20190065510A1
US20190065510A1
https://patents.google.com/patent/US20190065510A1
Methods and systems for processing a log file
United States
Disclosed are methods, apparatus, systems, and computer program products for provisioning log file data. To provision the log file data, a server can parse through log files by identifying particular log entries and data fields. Customer-facing log files can be generated based on the identified log entries and data fields. A customer-facing log file has log file data specific to a particular customer, which can be a tenant of a multi-tenant database system. A security mechanism can be configured to prevent one tenant from accessing the customer-facing log file of another tenant of the multi-tenant database system.
US10332129B2
US10332129B2
https://patents.google.com/patent/US10332129B2
Scheduling jobs for processing log files using a database system
United States
Disclosed are some examples of systems, methods, apparatus and computer program products for scheduling jobs to process log files. In some implementations, a scheduler can be configured to identify a final state of a previously scheduled first job as a failure in execution by one or more processors. For instance, the first job may have been defined to process a first input log file saved to a database during a first timeframe of a sequence of timeframes of a designated interval. The scheduler can detect a second input log file saved to the database. The scheduler can then define a second job to process the first input log file and the second input log file and schedule the second job in association with a second timeframe of the sequence.
https://patents.google.com/patent/US10275281B2
https://patents.google.com/patent/US10275281B2
https://patents.google.com/patent/US10275281B2
System having in-memory buffer service, temporary events file storage system and backup events file uploader service
United States
Computer-implemented methods and systems are provided. The system includes a data store that is configured to store events in an event table, a temporary events file storage system (TEFSS), and a cluster of application servers. The cluster includes a first application server that generates events, and a second application server that includes an events file uploader service. When the first application server is unable to directly write events to the data store, an indirect events writer generates events file(s), and writes the events file(s) to the TEFSS. Each events file includes a plurality of events flushed from an in-memory buffer service at the first application server. When the events file uploader service determines that the first application server is inactive, it reads the events file(s) from the TEFSS, and writes the events from each of the events files to the data store.
US14976125
US14976125
https://www.google.com/patents/US9632849
System and method for generating and storing real-time analytics metric data using an in memory buffer service consumer framework
United States
In an example, a processing system of a database system may categorize event data taken from logged interactions of users with a multi-tenant information system to provide a metric. Event roll-up aggregate metrics used to provide the metric may be generated in connection with event capture. The processing system of the database system may periodically calculate the metric for a particular one of the tenants, and electronically store the periodically calculated metrics for accessing responsive to a query of the particular tenant.
US14841503
US14841503
https://www.google.com/patents/US20170060771A1
In-memory buffer service
United States
A capture service running on an application server receives events from a client application running on an application server to be stored in a data store and stores the events in an in-memory bounded buffer on the application server, the in-memory bounded buffer comprising a plurality of single-threaded segments, the capture service to write events to each segment in parallel. The in-memory bounded buffer provides a notification to a buffer flush regulator when a number of events stored in the in-memory bounded buffer reaches a predefined limit. The in-memory bounded buffer receive a request to flush the events in the in-memory bounded buffer from a consumer executor service. The consumer executor service consumes the events in the in-memory bounded buffer using a dynamically sized thread pool of consumer threads to read the segments of the bounded buffer in parallel, wherein consuming the events comprises writing the events directly to the data store.
US20160077798
US20160077798
http://www.google.com/patents/US20160077798
Memory efficient policy-based file deletion system
United States
https://patents.google.com/patent/US20190065510A1/en?inventor=Aakash+Pradeep&page=1
Processing log files using a database system
United States
Disclosed are some examples of database systems, methods, and computer program products for processing log files. In some implementations, a server of a database system accesses a log file having a release version. The log file includes data entries identifying system events corresponding to user activity. The server generates or updates, based on the release version of the log file, a metadata file indicating approved entry types and approved data. The server also generates or updates, based on the log file and the metadata file, customer-facing log files. Each customer-facing log file is associated with a corresponding customer entity capable of being serviced by the database system. Each customer-facing log file includes a subset of the entries and a subset of the data items of at least one of the entries.
https://patents.google.com/patent/US20160041976A1
Extraction and capture of information from customizable header
United States
A database system captures custom information of a header section associated with a logged interaction of a user. The database system may receive a hypertext transfer protocol (http) message including the header section and determine whether the header section includes a predetermined data pattern associated with the custom information added by a second application that is different than a first application which initiated the http message. The database system may extract the custom information from the header section in response to determining that the header section includes the predetermined data pattern. The custom information and event data extracted from the logged interaction of the user may be stored on the database system as a storage element.
http://www.google.com/patents/US20160080461
Distributed sorting of event log files
United States
In an example, composite keys for an event log may be provided. A partitioner may be configured to extract a natural key from the composite keys and distribute log lines of event log files to a plurality of reducer nodes based on a value of the natural key. A comparator may use a log time of the composite key to sort a received portion of the distributed log lines.
https://www.google.com/patents/US20160063044
Methods and systems for processing a log file
United States
Disclosed are methods, apparatus, systems, and computer program products for provisioning log file data. A server may parse through log files by selecting particular log entries and data fields. Customer-facing log files may be generated based on the selected log entries and data fields.
https://patents.google.com/patent/US20150127670A1
cloudfs
An HDFS compatible FileSystem to store data in different cloud object storage like S3, GCS. In my view, a block storage system for a local Hard disk/SSD or different cloud vendor's object storage requires a hierarchical File System layer to make it easy to use it for processing and analyzing the data. The current implementation is based on that idea and it also tries to separate the actual data storage from the metadata storage so that either of them can be scaled independently. I also believe that the current set of big data processing tools at our disposals like Spark and MapReduce are heavily based on HDFS compatible Filesystem and require a layer like the current File System to work with cloud object storage in an optimal and less complicated way. It would allow using 2 phase commit to make sure either customer gets all the data or nothing after a processing step by allowing an atomic rename operation.
https://github.com/AakashPradeep/cloudfs
xStorage
An embedded key value storage system with reliable distributed persistence. Current implementation uses HDFS as distributed persistence but idea is to use any distributed filesystem or object storage like S3 or GCS and it should be plug-gable for each storage. Since its embedded, there can be only one client application which can read and write from the storage system with strong consistency. Since persistence is distributed, client can move to different hosts as long as persisted data is accessible.

The design of xStorage system is highly influenced from Level DB and Rocks DB. It uses LSM to provide high write performance and reliability, and it will be regularly flushed to disk (persistence layer) based on size and time.
https://github.com/AakashPradeep/xStorage
P2PHDFS
The Peer to Peer Hadoop Distributed File System (P2PHDFS) is designed to store very large data sets reliably and to stream those data sets at high bandwidth to user applications. It has a reliable  distributed file system with automated replication. The main objectives of P2PHDFS is to attain maximum performance and reliability as the infrastructure gets bigger and to make the system highly fault-tolerant system, Which will have zero single point of failure.
https://sites.google.com/a/temple.edu/p2phdfs/
Performance and Failure Monitoring for Apache Hadoop Using Ganglia and Cacti Monitoring Systems
The Apache Hadoop is an open source framework
which uses simple programming model to process large data sets
across a cluster of commodity hardware. Since commodity hard-
ware is very prone to failure, monitoring the performance and
failure of Hadoop cluster become very important. In this paper
we evaluate the performance of Ganglia and Cacti monitoring
tools for Hadoop monitoring. We evaluate the CPU and memory
overhead incurred while using these tools. The experiment is
implemented on Temple University Cloud.
https://www.linkedin.com/in/lakshmanan-muthuraman-3b4620b
Lakshmanan Muthuraman
Principal Software Engineer/Technical Lead at Twilio Inc.
Mountain View, CA
https://in.linkedin.com/in/shobhitaagarwal
Shobhita Agarwal
Engineering @Twilio | Ex-founder, Travel startup | Ex-Amazon
Bengaluru
https://www.linkedin.com/in/dipeshc
Dipesh Chauhan
Principal Software Engineer at Twilio Inc.
San Francisco Bay Area
https://www.linkedin.com/in/scotthaines
Scott Haines
Distinguished Software Engineer, ED&A Nike
San Francisco Bay Area
https://www.linkedin.com/in/abhijaypatne
Abhijay P.
Software Engineer at Twilio
San Francisco Bay Area
https://www.linkedin.com/in/heng-li-29a99b67
Heng Li
Software Engineer
San Francisco Bay Area
https://www.linkedin.com/in/andrewryno
Andrew Ryno
Principal Software Engineer at ngrok Inc.
San Francisco Bay Area
https://www.linkedin.com/in/hubert-huipeng-ren-2468982b
Hubert (Huipeng) Ren
Principal Software Engineer at Twilio Inc.
Santa Clara, CA
https://www.linkedin.com/in/bhargav-guntamadugu-32063127
Bhargav Guntamadugu
Staff Software Engineer and Tech Lead at Twilio. We are Hiring!
Sunnyvale, CA
https://www.linkedin.com/in/kris-nova
Kris Nóva
Senior Principal Software Engineer at Twilio Inc.
San Francisco, CA
https://www.linkedin.com/in/dineshnitk
Dinesh Garg
Data Stuff @ Twilio - Hiring !!!
San Francisco Bay Area
https://www.linkedin.com/in/akshaypaw
Akshay Pawar
Software Engineer - Data Platform @ Twilio Inc.
San Francisco Bay Area
https://www.linkedin.com/in/priyankad93
Priyanka Deshmukh
Senior Software Engineer at Twilio, Inc.
Redmond, WA
https://www.linkedin.com/in/stevenhpham
Steven Pham
Principal Software Engineer at Twilio Inc.
Denver Metropolitan Area
https://www.linkedin.com/in/ctgoss
Christopher Goss
Principal Software Engineer at Twilio
San Francisco, CA
https://www.linkedin.com/in/devaraj-adimurthy
Devaraj Adimurthy
Principal Software Engineer at Oracle
Englewood, CO
https://www.linkedin.com/in/akshayjain90
Akshay Jain
Software Engineer at Robinhood
San Francisco Bay Area
LinkedIn User
      
          



            
              
        
              
  

    
        Aakash joined Big Data team at Salesforce to work on Hadoop and HBase. During initial few days he impressed me and everyone in the team with knowledge of Hadoop and Distributed Systems in general. He is very dedicated, knowledgeable and talented individual who is also a nice and fun to work with. He explained me his MS thesis P2PHDFS to make HDFS masterless which is very impressive to be a masters thesis. He also has done partial implementation of it changing core parts of HDFS. He quickly grasped Salesforce multi-tenant environment and proposed, designed also developed sorting of logs. He quickly learned HBase internals. He also proposed, designed and implemented metadata driven log parsing with map reduce with high performance and utilizing the underlying resources effectively. Way to go Aakash!! Moussa Taifi Ph.D.
      
          



            
              
        
              
  

    
        Aakash is a stellar, dedicated, and productive software engineer. I had the pleasure to work with him on a distributed data storage project where he brought a high level of expertise in software development and project management. His experience with distributed systems is considerably superior to other students in his class. The depth of his interest in maintainable and testable systems was very helpful for me personally, because he brought his industry experience to our research project. This infused the end result of our project with his excellent knowledge of object-oriented programming, design patterns and performance-reliability tradeoffs in distributed systems. I would highly recommend Aakash as a sure value for any software project.
Great leaders, customers and global team at this week’s VMware Worldwide Kickoff. Thanks to all. Laura Heisman, John Sabino, Zia Yusuf, Kit…
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Hello!My name is Vitaliy Georgiev and just three weeks ago I worked as a strategic director in a leading marketing agency AIMBULANCE based in Kiev,…
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I got roped into some LinkedIn drama yesterday...On a beautiful post about a mother who interviewed for a job but had her babysitter fall through…
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Aakash Pradeep
https://in.linkedin.com/in/aakash-pradeep-58a0129
Soft Eng. at Yahoo!
Bengaluru
Aakash Pradeep
https://in.linkedin.com/in/aakash-pradeep-9b59b610
Software engineer at Yahoo! R&D
Bengaluru
Aakash Pradeep
https://in.linkedin.com/in/aakash-pradeep-6294a0193
Music Teacher at Greenwood High International School
Bengaluru
Aakash Pradeep
https://ae.linkedin.com/in/aakash-pradeep-1187171ba
Student at Heriot-Watt University
Sharjah, United Arab Emirates
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Cloud Storage
https://www.linkedin.com/groups/132257
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The Official Association for Computing Machinery (ACM) Group
https://www.linkedin.com/groups/36836
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Int. ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC)
https://www.linkedin.com/groups/3338228
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Chicago Young Professionals
https://www.linkedin.com/groups/58475
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Amazon Careers
https://www.linkedin.com/groups/3358744
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Hadoop India
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Yahoo Employees and Alumni
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Algorithms (O)
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Hadoop Users
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OpenStack
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Software Design Patterns And Architecture
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Test Automation
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Salesforce Alumni Network
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Java Developers
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