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20 Best Data Management Podcasts of 2021

Are you wanting to learn more about data management ? Well you’ve come to the right place. This is a curated list of the best data management podcasts of 2021.

We have selected these podcasts for a variety of reasons, but they are all well worth a listen. We tried to select a variety of podcasts across the spectrum from hosts with a wide breadth of experience.

We are always keen to hear your feedback, if we have missed a podcast, tweet us @MagazineWelp and we will check it out!

Best Data Management Podcasts 2021

With thanks to ListenNotes, Crunchbase, SemRush and Ahrefs for providing the data to create and rank these podcasts.

Data Management Moment: Lost Art

  • Publisher: Data Management University
  • Total Episodes: 11

Podcast by Data Management University

Data Made to Matter by MIT Sloan School of Management

  • Publisher: Data Made to Matter
  • Total Episodes: 12

Data Made to Matter explores the business breakthroughs that come from applying data-based research to real-world challenges. In this new show from the MIT Sloan School of Management, host Neal Hartman interviews MIT’s innovative thinkers on a range of topics, from the rising costs of healthcare, to global poverty, to addressing climate change. Data Made to Matter showcases how data, a spirit of experimentation, and desire to invent the future can advance management practice and improve the world. Produced by the MIT Sloan Office of Communications. Learn more @MITSloan.

Data Management Moment: Lost Art

  • Publisher: Data Management University
  • Total Episodes: 11

Podcast by Data Management University

MediQuant’s Healthcare Legacy Data Management Podcast

  • Publisher: MediQuant
  • Total Episodes: 5

The latest in healthcare data management and news from the pioneers in data legacy management

Distributed Data Management (WT 2018/19) – tele-TASK

  • Publisher: Prof. Dr. Felix Naumann, Dr. Thorsten Papenbrock
  • Total Episodes: 23

The free lunch is over! Computer systems up until the turn of the century became constantly faster without any particular effort simply because the hardware they were running on increased its clock speed with every new release. This trend has changed and today’s CPUs stall at around 3 GHz. The size of modern computer systems in terms of contained transistors (cores in CPUs/GPUs, CPUs/GPUs in compute nodes, compute nodes in clusters), however, still increases constantly. This caused a paradigm shift in writing software: instead of optimizing code for a single thread, applications now need to solve their given tasks in parallel in order to expect noticeable performance gains. Distributed computing, i.e., the distribution of work on (potentially) physically isolated compute nodes is the most extreme method of parallelization. Big Data Analytics is a multi-million dollar market that grows constantly! Data and the ability to control and use it is the most valuable ability of today’s computer systems. Because data volumes grow so rapidly and with them the complexity of questions they should answer, data analytics, i.e., the ability of extracting any kind of information from the data becomes increasingly difficult. As data analytics systems cannot hope for their hardware getting any faster to cope with performance problems, they need to embrace new software trends that let their performance scale with the still increasing number of processing elements. In this lecture, we take a look a various technologies involved in building distributed, data-intensive systems. We discuss theoretical concepts (data models, encoding, replication, …) as well as some of their practical implementations (Akka, MapReduce, Spark, …). Since workload distribution is a concept which is useful for many applications, we focus in particular on data analytics.

ISG Scalable Data Management Series

  • Publisher: Michael J. Carey
  • Total Episodes: 11

ISG Scalable Data Management Series

IS623 Database Management

  • Publisher: Edward Aractingi
  • Total Episodes: 5

Lectures from Information Systems Database Management Class

Management of Database Systems – MIS 542 Archive

  • Publisher: Professor Te-Wei Wang
  • Total Episodes: 29

Management of Database Systems – MIS 542 Archive

Database Management

  • Publisher: iTunes U Admin
  • Total Episodes: 2

Database Management

Database Management Systems – Course Materials

  • Publisher: David McDonald
  • Total Episodes: 10

Database Management Systems – Course Materials

Distributed Data Management (ST 2021) – tele-TASK

  • Publisher: Dr. Thorsten Papenbrock
  • Total Episodes: 8

The free lunch is over! Computer systems up until the turn of the century became constantly faster without any particular effort simply because the hardware they were running on increased its clock speed with every new release. This trend has changed and today’s CPUs stall at around 3 GHz. The size of modern computer systems in terms of contained transistors (cores in CPUs/GPUs, CPUs/GPUs in compute nodes, compute nodes in clusters), however, still increases constantly. This caused a paradigm shift in writing software: instead of optimizing code for a single thread, applications now need to solve their given tasks in parallel in order to expect noticeable performance gains. Distributed computing, i.e., the distribution of work on (potentially) physically isolated compute nodes is the most extreme method of parallelization. Big data analytics and management are a multi-million dollar markets that grow constantly! The ability to control and utilize large amounts of data is the most valuable ability of today’s computer systems. Because data volumes grow so rapidly and with them the complexity of questions they should answer, data analytics, i.e., the ability of extracting any kind of information from the data becomes increasingly difficult. As data analytics systems cannot hope for their hardware getting any faster to cope with performance problems, they need to embrace new software trends that let their performance scale with the still increasing number of processing elements. In this lecture, we take a look at various technologies involved in building distributed, data-intensive systems. We start by discussing fundamental concepts in distributed computing, such das data models, encoding formats, messaging, data replication and partitioning, fault tollerance, and batch- and stream processing. In between, we consider different practical systems from the Big Data Landscape, such as Akka and Spark. In the end, we concentrate on data management aspects, such as distributed database management system architectures and distributed query optimization.

Federal Insights: Data Management

  • Publisher: Hubbard/PodcastOne
  • Total Episodes: 4

Data is one of those words or maybe it’s a concept that is talked about in every part of the public sector. But one thing is clear, data is the gas that runs the government. And with the rise of connected devices and 5G, the volume, velocity and variety of data only will increase.

Data Science Product Management

  • Publisher: Randy Lariar
  • Total Episodes: 2

Exploring the best practices, successes, and failures of implementing data science in organizations. From cutting-edge AI to new ways of visualizing data, hear from practicioners who focus on building valuable profucts while deploying advanced data techniques .

Distributed Data Management (WT 2019/20) – tele-TASK

  • Publisher: Dr. Thorsten Papenbrock
  • Total Episodes: 27

The free lunch is over! Computer systems up until the turn of the century became constantly faster without any particular effort simply because the hardware they were running on increased its clock speed with every new release. This trend has changed and today’s CPUs stall at around 3 GHz. The size of modern computer systems in terms of contained transistors (cores in CPUs/GPUs, CPUs/GPUs in compute nodes, compute nodes in clusters), however, still increases constantly. This caused a paradigm shift in writing software: instead of optimizing code for a single thread, applications now need to solve their given tasks in parallel in order to expect noticeable performance gains. Distributed computing, i.e., the distribution of work on (potentially) physically isolated compute nodes is the most extreme method of parallelization. Big Data Analytics is a multi-million dollar market that grows constantly! Data and the ability to control and use it is the most valuable ability of today’s computer systems. Because data volumes grow so rapidly and with them the complexity of questions they should answer, data analytics, i.e., the ability of extracting any kind of information from the data becomes increasingly difficult. As data analytics systems cannot hope for their hardware getting any faster to cope with performance problems, they need to embrace new software trends that let their performance scale with the still increasing number of processing elements. In this lecture, we take a look a various technologies involved in building distributed, data-intensive systems. We discuss theoretical concepts (data models, encoding, replication, …) as well as some of their practical implementations (Akka, MapReduce, Spark, …). Since workload distribution is a concept which is useful for many applications, we focus in particular on data analytics.

[DIM] Data and Information Management

  • Publisher: BINUS University
  • Total Episodes: 10

Credit title: Subject Matter Expert : 1. Edi Purnomo Putra, S.Kom., M.MSI 2. Lay Christian, S.Kom., M.MSI Dokumenter: Binus University Uploaded by: Knowledge Management and Innovation Binus University

BINUS University: [DIM] Data and Information Management

  • Publisher: BINUS University: [DIM] Data and Information Management
  • Total Episodes: 10

BINUS University: [DIM] Data and Information Management (2019-07-11 07:13:33 +0000 UTC)

Database Management

  • Publisher: David Chu
  • Total Episodes: 3

Managing swarms of data, storing it and retrieving it perfectly for the future.

RDMRose – Research Data Management Case Studies

  • Publisher: The University of Sheffield
  • Total Episodes: 17

RDMRose – Research Data Management Case Studies

Data and Fiscal Management Monthly Webinars

  • Publisher: Various, Mississippi Department of Education
  • Total Episodes: 8

Data and Fiscal Management Monthly Webinars

PRT 504: Data Management and Applications in Park, Recreation, Tourism, and Sports Management

  • Publisher: Myron Floyd
  • Total Episodes: 17

PRT 504: Data Management and Applications in Park, Recreation, Tourism, and Sports Management

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