Overview of analytical on Hadoop

What's hot

VariantSpark on AWS

Lynn Langit

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Tim Bell

Everybody wants to do big data analytics these days: storage is cheapand data is plentiful; best of all, software in the Hadoop ecosystem is free both as in speech and as in beer. If you are not Facebook or Amazon, however, you are not likely to put your precious data in the systems of cloud providers you may not trust; on the other hand, developing your own small or medium cluster can be prohibitive, since it requires a lot of effort and specialization to be deployed, tuned and maintained. BigFoot aims to simplify the data scientist's life, making the existing big data software easier to deploy and tune, so that data scientists can focus on their job: getting insight from data. BigFoot contributes to OpenStack: we made it possible to deploy virtualized Spark clusters, enabling analytics-as-a-service using fast in-memory computation. HFSP, our scheduler for Hadoop Mapreduce, gives priority to smaller jobs, so that large batch jobs do not harm user productivity by slowing down quicker data exploration jobs. Interestingly, HFSP achieves this without penalizing large jobs. We also contribute to the Apache Pig high-level analytics language: we propose patches that strongly enhance performance when computing aggregations on multi-dimensional data.

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Matteo Dell'Amico

Building Hadoop Data Applications with Kite

huguk

Architecting R into the Storm Application Development Process ~~~~~ The business need for real-time analytics at large scale has focused attention on the use of Apache Storm, but an approach that is sometimes overlooked is the use of Storm and R together. This novel combination of real-time processing with Storm and the practical but powerful statistical analysis offered by R substantially extends the usefulness of Storm as a solution to a variety of business critical problems. By architecting R into the Storm application development process, Storm developers can be much more effective. The aim of this design is not necessarily to deploy faster code but rather to deploy code faster. Just a few lines of R code can be used in place of lengthy Storm code for the purpose of early exploration – you can easily evaluate alternative approaches and quickly make a working prototype. In this presentation, Allen will build a bridge from basic real-time business goals to the technical design of solutions. We will take an example of a real-world use case, compose an implementation of the use case as Storm components (spouts, bolts, etc.) and highlight how R can be an effective tool in prototyping a solution.

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Allen Day, PhD

Apache Hive facilitates querying and managing large datasets residing in distributed storage. Being used by a very wide community, Hive has been extended to support multiple distributed storage systems. It is now a common practice to have data in different storage systems within an organization. This presentation covers two important aspects of Apache Hive. First aspect covers how Hive makes it possible for organizations to run complex analytical queries across various storage systems or big data components. We recently added HiveKa, to support hive queries on Kafka, and will use it as an example. At Cloudera, we focus not only on providing solutions to help organizations answer bigger questions, but we also make sure that these solutions are robust. The second aspect of this presentation goes over advanced methods/ technologies, like, Random Query Generators, Dockers, Benchmarks, etc that we use at Cloudera to make sure Hive is ready to find right answers from that huge Volume, high Velocity and various Varieties of today’s data.

Querying multiple distributed storage systems with Apache Hive robustly

Ashish Singh

By Kevin Schmidt (Head of Data Science at Mind Candy) Luis Vicente (Senior Data Engineer at Mind Candy) For mobile games, constant tweaks are the difference between success and failure. Data and analytics have to be available in real-time, but calculating, for example, uniqueness or newness of a data point requires a list of seen data points - both memory intensive and tricky when using real-time stream processing like Spark Streaming. Probabilistic data structures allow approximation of these properties with a fixed memory representation, and are very well suited for this kind of stream processing. Getting from the theory of approximation to a useful metric at a low error rate even for many millions of users is another story. In our talk we will look at practical ways of achieving this: which approximation we used for selection of useful metrics, why we picked a specific probabilistic data structure, how we stored it in Cassandra as a time series and how we implemented it in Spark Streaming.

Fast real-time approximations using Spark streaming

huguk

Apache Giraph performs offline, batch processing of very large graph datasets on top of a Hadoop cluster. Giraph replaces iterative MapReduce-style solutions with Bulk Synchronous Parallel graph processing using in-memory or disk-based data sets, loosely following the model of Google`s Pregel. Many recent advances have left Giraph more robust, efficient, fast, and able to accept a variety of I/O formats typical for graph data in and out of the Hadoop ecosystem. Giraph's recent port to a pure YARN platform offers increased performance, fine-grained resource control, and scalability that Giraph atop Hadoop MRv1 cannot, while paving the way for ports to other platforms like Apache Mesos. Come see whats on the roadmap for Giraph, what Giraph on YARN means, and how Giraph is leveraging the power of YARN to become a more robust, usable, and useful platform for processing Big Graph datasets.

Fast, Scalable Graph Processing: Apache Giraph on YARN

DataWorks Summit

The Elephant in the Cloud: A Quest for the Next Generation In this talk, I will go through the evolution of Hadoop and its ecosystem projects and will try to peer into the crystal ball to predict what may be coming down the pike. I will discuss various way of crunching the data on Hadoop (MapReduce, OpenMPI, Spark and various SQL engines) and how these tools compliment each other. Apache Hadoop is no longer just a faithful, open source, scalable implementation of two seminal papers that came out of Google 10 years ago. It has evolved into a project that provides the enterprises with a reliable layer for storing massive amounts of unstructured data (HDFS) while allowing different computational frameworks to leverage those datasets. The original computational framework (MapReduce) has evolved into a much more scalable set of general purpose cluster management APIs collectively known as YARN. With YARN underneath, MapReduce is still there to support batch-oriented computations, but it is no longer the only game in town. With OpenMPI, Spark, and Tez rapidly becoming available now is truly the most exciting time to be a developer in a Hadoop ecosystem. It is also the time when you don't have to be employed by Yahoo!, Facebook or EBay to have access to mind-blowing compute power. That power is a credit card and a pivotal.io account away from anybody on the planet. I will conclude by outlining some of the ongoing work that makes Hadoop and its ecosystem projects first class citizens in cloud environments based on the work that Pivotal engineers have done with integrating Hadoop into PivotalONE PaaS.

Elephant in the cloud

rhatr

Apache Spark: killer or savior of Apache Hadoop?

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As Netflix expands their services to more countries, devices, and content, they continue to evolve their big data analytics platform to accommodate the increasing needs of product and consumer insights. This year, Netflix re-innovated their big data platform: they upgraded to Hadoop 2, transitioned to the Parquet file format, experimented with Pig on Tez for the ETL workload, and adopted Presto as their interactive querying engine. In this session, Netflix discusses their latest architecture, how they built it on the Amazon EMR infrastructure, the contributions put into the open source community, as well as some performance numbers for running a big data warehouse with Amazon S3.

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Python analytics from HPC to public object stores

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Kafka Lambda architecture with mirroring

Anant Rustagi

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Krzysztof Adamski

Wisely Chen Spark Talk At Spark Gathering in Taiwan

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Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...

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VariantSpark on AWS

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BigFoot: Big Data For Every Organization

Building Hadoop Data Applications with Kite

R + Storm Moneyball - Realtime Advanced Statistics - Hadoop Summit - San Jose

Querying multiple distributed storage systems with Apache Hive robustly

Fast real-time approximations using Spark streaming

Fast, Scalable Graph Processing: Apache Giraph on YARN

Elephant in the cloud

Apache Spark: killer or savior of Apache Hadoop?

(BDT403) Netflix's Next Generation Big Data Platform | AWS re:Invent 2014

Python analytics from HPC to public object stores

Blueflood and Beyond: The Future of Metrics - Berlin Buzzwords 2014

Kafka Lambda architecture with mirroring

Elephants in the cloud or how to become cloud ready

Wisely Chen Spark Talk At Spark Gathering in Taiwan

Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...

Blueflood: Open Source Metrics Processing at CassandraEU 2013

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Advanced Analytics using Apache Hive

Murtaza Doctor

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Hive Query Language (HQL) is excellent for productivity and enables reuse of SQL skills, but falls short in advanced analytic queries. Hive`s Map & Reduce scripts mechanism lacks the simplicity of SQL and specifying new analysis is cumbersome. We developed SQLWindowing for Hive(SQW) to overcome these issues. SQW introduces both Windowing and Table Functions to the Hive user. SQW appears as a HQL extension with table functions and windowing clauses interspersed with HQL. This means the user stays within a SQL-like interface, while simultaneously having these capabilities available. SQW has been published as an open source project. It is available as both a CLI and an embeddable jar with a simple query API. There are pre-built functions for windowing to do Ranking, Aggregation, Navigation and Linear Regression. There are Table functions to do Time Series Analysis, Allocations, and Data Densification. Functions can be chained for more complex analysis. Under the covers MR mechanics are used to partition and order data. The fundamental interface is the tableFunction, whose core job is to operate on data partitions. Function implemenations are isolated from MR mechanics, focus purely on computation logic. Groovy scripting can be used for core implementation and parameterizing behavior. Writing functions typically involves extending one of the existing Abstract functions.

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These are the slides of my OpenSIPS Summit 2024 presentation about automating your test calls. It dives into why automated call testing is crucial, how to integrate it into your CI/CD pipeline and how to extend testing of single calls into load testing and testing of other protocols. The presentation also provides details how open source components such as sipp, asterisk and rtpengine are used to implement agents to generate test calls for SIP, Fax and WebRTC at scale.

Automate your OpenSIPS config tests - OpenSIPS Summit 2024

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Nearly a decade ago, a small group of menders (and one maker) found community with each other at a conference and Legacy Code Rocks was born. Since then, the Legacy Code Rocks community has grown to over 1000 people, 150 podcast episodes, a weekly meetup and of course, MenderCon. In this talk, Andrea Goulet, the maker who kindled the spark, will take us back to those early days and reflect on how the bedrock of the Legacy Code Rocks community has always been creating a sense of belonging. Compassion and empathy are at the core of being a mender. Mending is caring deeply, seeing potential, and being willing to work through the frustration of making things better one small step at a time. This commitment to helping others feel seen instead of shame is why the Legacy Code Rocks community is so special — and it’s exactly why having the heart of the mender is what our world needs now more than ever.

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It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions. This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.

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Azure Native Qumulo scales elastically for common High Performance Compute (HPC) workloads based on application requirements for: Financial Services, Automotive, Genomics / Life Sciences, Media and Entertainment, Energy, Oil and Gas, and more. Performance can be increased (and elastically decreased) much higher than the examples shown here. These slides offer a glimpse into ANQ's HPC capabilities, although at a smaller scale. We invite YOU to do your own testing (with a free ANQ trial) and work with us to test your HPC workloads in Azure.

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The Mythical Technical Debt (Brooke, please, forgive me.) <This talk is designed for a technical audience> In software-intensive systems development, we often face trade-offs between speed and long-term maintainability. These trade-offs accumulate as technical debt, the hidden cost of short-term decisions that can impact future development. Technical debt goes beyond the initial choices made when creating software. It encompasses design decisions, coding practices, or anything that might hinder future development efforts. By effectively identifying and managing this debt, we can build robust and scalable software systems. In this talk, we'll explore different types of technical debt and their impact on projects. We'll delve into practical methods to identify, measure, and reduce it. Finally, we'll see how to turn technical debt into an advantage, using it as a springboard for innovation with real-world examples.

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Computation is increasingly constrained by power. With each advancement in the manufacturing process, a decreasing percentage of the CPU can operate at full capacity, leading to the emergence of the term 'dark silicon'. This trend necessitates techniques that utilize chip area to optimize power efficiency through specialized accelerators. The presentation will outline key concepts that led to the dark silicon such as Moore’s law and breakdown of Dennard scaling, followed by an overview of current and upcoming CPU accelerators. The focus will then shift to vector units and the specifics of vector programming. Attendees will be introduced to registers, a range of vector operations, and methods to develop branchless algorithms such as sorting networks. The session will conclude with an overview of the new Java Vector API and how it was already picked up by projects to do AI inference (Llama 2) and vector search (AstraDB and Cassandra).

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