Tackling Big Data with the Elephant in the Room
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This document discusses Hadoop and its core components HDFS and MapReduce as a solution to big data problems. HDFS stores large data files across clusters of computers as blocks that can be processed in parallel by MapReduce. MapReduce allows distributed processing of large datasets by mapping input data to intermediate key-value pairs, shuffling and sorting the data, and reducing it to final results. Hadoop provides scalable and cost-effective solutions to challenges like volume, variety, and velocity of big data.
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It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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Tackling Big Data with the Elephant in the Room
- 1. TACKLING BIG DATA WITH THE ELEPHANT IN THE ROOM
- 2. WHAT’S THE PROBLEM WITH BIG DATA? Volume VarietyVelocity
- 3. WHAT’S THE SOLUTION TO BIG DATA? “In pioneer days they used oxen for heavy pulling, and when one oxen couldn’t budge a log, they didn’t try to grow a larger ox. We shouldn’t be trying for bigger computers, but for more systems of computers.” – Grace Hopper
- 4. HADOOP’S SOLUTION Sqoop Pig Hive HBase Mahout Flume Oozie … Hadoop Distributed File System MapReduce Hadoop Core Components Hadoop Ecosystem
- 6. HOW DOES HDFS WORK? Large Data File Block #1 Block #2
- 7. HOW DOES HDFS WORK? Large Data File Block #1 Block #2 Block #1 Block #1 Block #1
- 8. HOW DOES HDFS WORK? Large Data File Block #1 Block #2 Block #1 Block #1 Block #1 Block #2 Block #2 Block #2
- 9. HOW DOES HDFS WORK? Large Data File Block #1 Block #2 Block #1 Block #1 Block #1 Block #2 Block #2 Block #2
- 10. WHAT IS MAP-REDUCE? Core Ideas – Data Locality – Parallelism – Block Independence Three Stages 1. Map 2. Swap & Sort 3. Reduce
- 11. WORD COUNT MAP the cat sat on the mat the aardvark sat on the … Node 1 the mahout drove the …. Node 2 the cat sat on the mat The aardvark sat on the … The mahout drove the …
- 12. Mapper WORD COUNT MAP the cat sat on the mat the aardvark sat on the … Node 1 the mahout drove the …. Node 2 Mapper map() map()
- 13. Mapper WORD COUNT MAP the cat sat on the mat the aardvark sat on the … Node 1 the mahout drove the …. Node 2 Mapper map() map() the 1 cat 1 sat 1 on 1 the 1 mat 1 the 1 mahout 1 drove 1 the 1
- 14. Mapper WORD COUNT MAP the cat sat on the mat the aardvark sat on the … Node 1 the mahout drove the …. Node 2 Mapper map() map() the 1 cat 1 sat 1 on 1 the 1 mat 1 the 1 mahout 1 drove 1 the 1 map() the 1 aardvark 1 sat 1 on 1 the 1
- 15. WORD COUNT SWAP & SORT the 1 cat 1 sat 1 on 1 the 1 mat 1 the 1 mahout 1 drove 1 the 1 the 1 aardvark 1 sat 1 on 1 the 1
- 16. WORD COUNT SWAP & SORT the 1 cat 1 sat 1 on 1 the 1 mat 1 the 1 mahout 1 drove 1 the 1 the 1 aardvark 1 sat 1 on 1 the 1 aardvark 1 cat 1 mat 1 on 1,1 sat 1 the 1,1,1,1 drove 1 mahout 1 the 1,1
- 17. WORD COUNT SWAP & SORT the 1 cat 1 sat 1 on 1 the 1 mat 1 the 1 mahout 1 drove 1 the 1 the 1 aardvark 1 sat 1 on 1 the 1 aardvark 1 cat 1 mat 1 on 1,1 sat 1 the 1,1,1,1 drove 1 mahout 1 the 1,1 aardvark 1 cat 1 mat 1 mahout 1 sat 1 drove 1 on 1,1 the 1,1,1,1,1,1 Node 3 Node 4 Node 5
- 18. WORD COUNT REDUCER aardvark 1 cat 1 mat 1 mahout 1 sat 1 drove 1 on 1,1 the 1,1,1,1,1,1 Node 3 Node 4 Node 5 Reducer 0 Reducer 1 Reducer 2 aardvark 1 cat 1 mat 1 mahout 1 sat 1 drove 1 on 2 the 6
- 19. TAKE-AWAYS Sqoop Pig Hive HBase Mahout Flume Oozie … Hadoop Distributed File System MapReduce Hadoop Core Components Hadoop Ecosystem
- 20. QUESTIONS?