Open Source Tools for Big Data
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Exove Extends September 19th, 2017 Open Source Tools for Big Data, Teemu Heikkilä, Emblica Short introduction to open source tools around big data analytics and development with such tools
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Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
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This document introduces big data by defining it as large, complex datasets that cannot be processed by traditional methods due to their size. It explains that big data comes from sources like online activity, social media, science, and IoT devices. Examples are given of the massive scales of data produced each day. The challenges of processing big data with traditional databases and software are illustrated through a fictional startup example. The document argues that new tools and approaches are needed to handle automatic scaling, replication, and fault tolerance. It presents Apache Hadoop and Spark as open-source big data tools that can process petabytes of data across thousands of nodes through distributed and scalable architectures.
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Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
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The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
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Bigdata
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
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Bp presentation business intelligence and advanced data analytics september ...
Bp presentation business intelligence and advanced data analytics september ...
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...
Similar to Open Source Tools for Big Data
NoSQL and Hadoop: A New Generation of Databases - Changing the Game: Monthly ...
NoSQL and Hadoop databases are emerging as alternatives to traditional relational databases for handling large amounts of unstructured data from sources like the cloud and web. Major tech companies like Oracle, IBM, Microsoft, EMC, Google and Amazon support NoSQL, with many choosing Apache Hadoop. Hadoop is an open source NoSQL database that can handle huge amounts of unstructured data at scale in cloud environments. It was designed to be fully distributed like Google's MapReduce and uses Java for integration. Relational databases remain effective for structured applications but face challenges with unstructured data, scale, and cloud deployments.
963
This document provides an overview of real time big data processing using Apache Kafka, Spark Streaming, Scala, and Elastic search. It begins with introductions to data mining, big data, and real time big data. It then discusses Apache Hadoop, Scala, Spark Streaming, Kafka, and Elastic search. The key technologies covered allow for distributed, low latency processing of streaming data at large volumes and velocities.
Present and future of unified, portable, and efficient data processing with A...
The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
Davor Bonaci, Apache Software Foundation; Simbly, V.P. of Apache Beam; Founder/CEO at Operiant
The Big Data Puzzle, Where Does the Eclipse Piece Fit?
A discussion of our experience with integrating Eclipse and Apache projects into a big data analysis capability.
Hadoop
Hadoop is an open-source framework for storing and processing large datasets in a distributed computing environment. It allows for massive data storage, enormous processing power, and the ability to handle large numbers of concurrent tasks across clusters of commodity hardware. The framework includes Hadoop Distributed File System (HDFS) for reliable data storage and MapReduce for parallel processing of large datasets. An ecosystem of related projects like Pig, Hive, HBase, Sqoop and Flume extend the functionality of Hadoop.
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
This introductory level talk is about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source.
With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing.
In this talk, you will learn about:
1. What is Apache Flink stack and how it fits into the Big Data ecosystem?
2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment?
3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark.
4. Who is using Apache Flink?
5. Where to learn more about Apache Flink?
Big data apache spark + scala
This document provides an overview of Spark, including:
- Spark was developed in 2009 at UC Berkeley and open sourced in 2010, with over 200 contributors.
- Spark Core is the general execution engine that other Spark functionality is built on, providing in-memory computing and supporting various programming languages.
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The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
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Database Integrated Analytics using R InitialExperiences wi
Database Integrated Analytics using R: Initial
Experiences with SQL-Server + R
Josep Ll. Berral and Nicolas Poggi
Barcelona Supercomputing Center (BSC)
Universitat Politècnica de Catalunya (BarcelonaTech)
Barcelona, Spain
Abstract—Most data scientists use nowadays functional or
semi-functional languages like SQL, Scala or R to treat data,
obtained directly from databases. Such process requires to fetch
data, process it, then store again, and such process tends to
be done outside the DB, in often complex data-flows. Recently,
database service providers have decided to integrate “R-as-a-
Service” in their DB solutions. The analytics engine is called
directly from the SQL query tree, and results are returned as
part of the same query. Here we show a first taste of such
technology by testing the portability of our ALOJA-ML analytics
framework, coded in R, to Microsoft SQL-Server 2016, one of
the SQL+R solutions released recently. In this work we discuss
some data-flow schemes for porting a local DB + analytics engine
architecture towards Big Data, focusing specially on the new
DB Integrated Analytics approach, and commenting the first
experiences in usability and performance obtained from such
new services and capabilities.
I. INTRODUCTION
Current data mining methodologies, techniques and algo-
rithms are based in heavy data browsing, slicing and process-
ing. For data scientists, also users of analytics, the capability
of defining the data to be retrieved and the operations to be
applied over this data in an easy way is essential. This is the
reason why functional languages like SQL, Scala or R are so
popular in such fields as, although these languages allow high
level programming, they free the user from programming the
infrastructure for accessing and browsing data.
The usual trend when processing data is to fetch the data
from the source or storage (file system or relational database),
bring it into a local environment (memory, distributed workers,
...), treat it, and then store back the results. In such schema
functional language applications are used to retrieve and slice
the data, while imperative language applications are used to
process the data and manage the data-flow between systems.
In most languages and frameworks, database connection pro-
tocols like ODBC or JDBC are available to enhance this data-
flow, allowing applications to directly retrieve data from DBs.
And although most SQL-based DB services allow user-written
procedures and functions, these do not include a high variety
of primitive functions or operators.
The arrival of the Big Data favored distributed frameworks
like Apache Hadoop and Apache Spark, where the data is
distributed “in the Cloud” and the data processing can also be
distributed where the data is placed, then results are joined
and aggregated. Such technologies have the advantage of
distributed computing, but when the schema for accessing data
and using it is still the same, ...
RDBMS vs Hadoop vs Spark
A brief about Relational Database Management System (RDBMS), Apache Spark, and Hadoop and a comparison between them.
Big dataanalyticsinthecloud
Siva Narayanan presents on big data analytics in the cloud using Qubole. Traditional big data projects require buying hardware, software, and hiring people to manage it, often resulting in underutilized clusters. Qubole offers a platform for interactive big data analytics in the cloud, automating cluster management and providing optimizations to improve performance. It integrates with various data sources and tools, and allows scheduling workflows and analysis. Qubole aims to make big data analytics easier, more cost-effective and faster in the cloud.
Is multi-model the future of NoSQL?
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Hopsworks - The Platform for Data-Intensive AI
Hopsworks is a platform for data-intensive AI projects that provides:
1. End-to-end machine learning pipelines from data ingestion to model serving.
2. A feature store for organizing machine learning data.
3. Distributed deep learning using multiple GPUs for faster training.
Big Data, Ingeniería de datos, y Data Lakes en AWS
Epic Games uses AWS services extensively to gain insights from player data and ensure Fortnite remains engaging for its over 125 million players. Telemetry data from clients is collected with Kinesis and analyzed in real-time using Spark on EMR. Game designers use these insights to inform decisions. Epic also uses S3 as a data lake, DynamoDB for real-time queries, and EMR for batch processing. This analytics platform on AWS allows constant feedback to optimize the player experience.
Extending DevOps to Big Data Applications with Kubernetes
DevOps, continuous delivery and modern architectural trends can incredibly speed up the software development process. Big Data applications cannot be an exception and need to keep the same pace.
The BDAS Open Source Community
AMPCamp 5 presentation on "The BDAS Open Source Community" by Prof. Ion Stoica of the UC Berkeley AMPLab and Databricks
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NoSQL and Hadoop: A New Generation of Databases - Changing the Game: Monthly ...
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Present and future of unified, portable, and efficient data processing with A...
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The Big Data Puzzle, Where Does the Eclipse Piece Fit?
The Big Data Puzzle, Where Does the Eclipse Piece Fit?
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TDC2017 | POA Trilha BigData - IBM BigSQL - Engine de consulta de dados de al...
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Recent IT Development and Women: Big Data and The Power of Women in Goryeo
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Present and future of unified, portable and efficient data processing with Ap...
Present and future of unified, portable and efficient data processing with Ap...
Database Integrated Analytics using R InitialExperiences wi
Database Integrated Analytics using R InitialExperiences wi
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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Open Source Tools for Big Data
- 1. OPEN SOURCE TOOLS FOR BIG DATA Helsinki 19.9.2017 Teemu Heikkilä Emblica
- 2. EMBLICA We’re super small company of 5 people We’re into Data Engineering, DevOps and ML We’re hiring!
- 3. Let’s start with something simple first
- 4. What is “big data”?
- 6. …but, still it has a meaning.
- 7. 1TB of data, is it BIG?
- 10. We are not really in Facebook scale but is it worth to talk about big data tools?
- 11. Answer: Yes!
- 12. Question: Why?
- 13. Because what works with petabytes of data, almost certainly works with gigabytes
- 14. Helsinki City bike station usage 17M rows of JSON
- 15. You will get: Fault tolerance, reliability, scalability and working models of processing data of any amounts
- 16. … but it doesn't mean you need fancy frameworks necessarily
- 18. History of data processing with free software
- 19. NOW200320011997 2006 Google published whitepaper about solving storage problems with web indexing. Carafella and Cutting implemented the white paper as part of the Nutch project GFS HISTORY OF HADOOP Doug Cutting started to develop first version of Lucene at Yahoo! START Cutting moved the NDFS and MapReduce related codebase under new project called Hadoop HADOOP Cutting open sourced Lucene and it was moved under Apache Foundation Mike Cafarella joined with Cutting to start Apache Nutch - project to index whole internet. OPEN SOURCED
- 22. Ideas, (whitepapers) DFS MR BigTable Dynamo FOSS Implementations HDFS Hadoop MR HBase Cassandra
- 23. (Notable) formats of Big data
- 24. ACTIVITY DATA Clickstreams App usage Application specific usage Music listening Video streaming Money usage Credit cards Transactions
- 25. SENSOR DATA Locations Spatial data Sensor metrics IoT devices Industrial and consumer Time series
- 26. UNSTRUCTURED DATA Machine logs, Unstructured text, natural language Sound, Photos, Video
- 27. Use cases What are you using those fancy logos for?
- 28. CASE 1: EVENT SOURCING SQL-DATABASES Working legacy systems that used MySQL-database as a realtime data storage. No historical data saved ever. Delete means delete Update means update We could touch the legacy code to save the changes But we don’t have to
- 29. Maxwell’s daemon Reads MySQL replication binary log Produces stream of JSON-formatted changes
- 30. ?
- 31. KAFKA - DISTRIBUTED APPEND-ONLY LOG Kafka was originally developed by LinkedIn, open sourced 2011 Distributed, append-only log Great tool for delivering reliably millions of arbitrary formatted messages Scales by partitioning and adding new nodes (c) Ch.ko123 / CC BY 4.0
- 32. (c) Apache Spark + Fast writes (queue/log) + Fast reads (in-memory) - Latency - Reliable event delivery is essential KAPPA ARCHITECTURE
- 33. MATERIALIZING EVENT SOURCES Change stream Change stream Change stream Materialized ‘User’-table Materialized ‘Resource’-table Materialized ‘Usage’-table
- 35. APACHE SPARK Originally developed at the University of California, Berkeley's AMPLab General large-scale data processing framework Based on MapReduce architecture but keeps intermediate results in memory instead of saving them to slow disks like Hadoop (c) Ch.ko123 / CC BY 4.0 Supports lot’s of different data sources Programming APIs for Scala, Java or Python
- 36. EKS-STACK Elasticsearch is based on Lucene but it’s more than just search engine, it can be used to provide real time analytics even for end users, it’s usually used to store the aggregated data Kibana is great tool for the developers and for internal use to discover and analyze the data lying inside ES Spark is used to process the events, produce the needed aggregates and ingest data into Elasticsearch so it can be queried
- 38. Event Collector Processing AnalyticsEventsUser agent CASE 2: EVERY ANALYTICS PIPELINE EVER
- 40. What are we sampling?
- 43. New session: started 07:17:09, duration 0s, OPEN Existing session: started 07:17:09, duration 5s, OPEN Existing session: started 07:17:09, duration 10s, OPEN Existing session: started 07:17:09, duration 14s, paused 07:17:23, CLOSED
- 44. New session: started 07:17:09, duration 0s, OPEN Existing session: started 07:17:09, duration 5s, OPEN Existing session: started 07:17:09, duration 10s, OPEN Existing session: started 07:17:09, duration 14s, paused 07:17:23, CLOSED
- 45. You can find me at: @theikkilap teemu@emblica.fi https://emblica.fi Any questions? Thanks! Icons from Font Awesome project