The document discusses how a client's analytics website was improved by replacing Oracle with Hadoop and Solr. The website previously took 10-30 seconds for UI refreshes and had expensive hardware costs. By using Hadoop to pre-generate analytics offline and Solr as a NoSQL database, the new system provided faster responses under 1 second, lower costs, and better scalability. The document outlines the ETL process and optimizations used to transform raw data into a Solr index for fast querying of pre-calculated answers.
Cloud also presents us with capabilities to enable IT to break out of silos and deliver real results on customer and organizational needs. Value creation processes are dynamic, flexible and multi-dimensional to meet the changing needs of the customer and your organization. Dell believes that cloud can be a ‘change agent’, helping to overcome capacity limitations and drive value creation rather than just reactive servicing.
With OpenStack and OpenSource, customers can get further cost benefits by not having to pay exhorbitant license fees and also get locked into proprietary vendor stacks and API/s
Neustar is a fast growing provider of enterprise services in telecommunications, online advertising, Internet infrastructure, and advanced technology. Neustar has engaged Think Big Analytics to leverage Hadoop to expand their data analysis capacity. This session describes how Hadoop has expanded their data warehouse capacity, agility for data analysis, reduced costs, and enabled new data products. We look at the challenges and opportunities in capturing 100′s of TB’s of compact binary network data, ad hoc analysis, integration with a scale out relational database, more agile data development, and building new products integrating multiple big data sets.
Scalable and Elastic Transactional Data Stores for Cloud Computing Platformssudiptdas
Cloud computing has emerged as a multi-billion dollar industry and as a successful paradigm for web application deployment. Economies-of-scale, elasticity, and pay-per-use pricing have been the biggest promises of cloud. Database management systems (DBMSs) serving these web applications form a critical component of the cloud software stack. These DBMSs must be able to scale-out to clusters of commodity servers to serve thousands of applications and their huge amounts of data. Moreover, to minimize the operating costs such DBMSs must also be elastic, i.e. posses the ability to increase and decrease the cluster size in a live system. This is in addition to serving a variety of applications (i.e. support multitenancy) while being self-managing, fault-tolerant, and highly available.
The overarching goal of my dissertation is to propose abstractions, protocols, and paradigms to design scalable and elastic database management systems that address the unique set of challenges posed by the cloud. My dissertation shows that with careful choice of design and features, it is possible to architect scalable DBMSs that efficiently support transactional semantics to ease application design and elastically adapt to fluctuating operational demands to optimize the operating cost. In this talk, I will outline my work that embodies this principle. In the first part, I will present techniques and system architectures to enable efficient and scalable transaction processing on clusters of commodity servers. In the second part, I will present techniques for on-demand database migration in a live system, a primitive operation critical to support lightweight elasticity as a first class feature in DBMSs. I will conclude the talk with a discussion of possible future directions.
Application Development & Database Choices: Postgres Support for non Relation...EDB
This talk will cover the advanced features of PostgreSQL that make it the most-loved RDBMS by developers and a great choice for non-relational workloads.
This webinar will explore:
- Global adoption of Postgres
- Document-centric applications
- Geographic Information Systems (GIS)
- Business intelligence
- Central data centers
- Server-side languages
Cloud also presents us with capabilities to enable IT to break out of silos and deliver real results on customer and organizational needs. Value creation processes are dynamic, flexible and multi-dimensional to meet the changing needs of the customer and your organization. Dell believes that cloud can be a ‘change agent’, helping to overcome capacity limitations and drive value creation rather than just reactive servicing.
With OpenStack and OpenSource, customers can get further cost benefits by not having to pay exhorbitant license fees and also get locked into proprietary vendor stacks and API/s
Neustar is a fast growing provider of enterprise services in telecommunications, online advertising, Internet infrastructure, and advanced technology. Neustar has engaged Think Big Analytics to leverage Hadoop to expand their data analysis capacity. This session describes how Hadoop has expanded their data warehouse capacity, agility for data analysis, reduced costs, and enabled new data products. We look at the challenges and opportunities in capturing 100′s of TB’s of compact binary network data, ad hoc analysis, integration with a scale out relational database, more agile data development, and building new products integrating multiple big data sets.
Scalable and Elastic Transactional Data Stores for Cloud Computing Platformssudiptdas
Cloud computing has emerged as a multi-billion dollar industry and as a successful paradigm for web application deployment. Economies-of-scale, elasticity, and pay-per-use pricing have been the biggest promises of cloud. Database management systems (DBMSs) serving these web applications form a critical component of the cloud software stack. These DBMSs must be able to scale-out to clusters of commodity servers to serve thousands of applications and their huge amounts of data. Moreover, to minimize the operating costs such DBMSs must also be elastic, i.e. posses the ability to increase and decrease the cluster size in a live system. This is in addition to serving a variety of applications (i.e. support multitenancy) while being self-managing, fault-tolerant, and highly available.
The overarching goal of my dissertation is to propose abstractions, protocols, and paradigms to design scalable and elastic database management systems that address the unique set of challenges posed by the cloud. My dissertation shows that with careful choice of design and features, it is possible to architect scalable DBMSs that efficiently support transactional semantics to ease application design and elastically adapt to fluctuating operational demands to optimize the operating cost. In this talk, I will outline my work that embodies this principle. In the first part, I will present techniques and system architectures to enable efficient and scalable transaction processing on clusters of commodity servers. In the second part, I will present techniques for on-demand database migration in a live system, a primitive operation critical to support lightweight elasticity as a first class feature in DBMSs. I will conclude the talk with a discussion of possible future directions.
Application Development & Database Choices: Postgres Support for non Relation...EDB
This talk will cover the advanced features of PostgreSQL that make it the most-loved RDBMS by developers and a great choice for non-relational workloads.
This webinar will explore:
- Global adoption of Postgres
- Document-centric applications
- Geographic Information Systems (GIS)
- Business intelligence
- Central data centers
- Server-side languages
Hadoop Summit 2012 | Integrating Hadoop Into the EnterpriseCloudera, Inc.
The power of Hadoop lies in its ability to help users cost effectively analyze all kinds of data. We are now seeing the emergence of a new class of analytic applications that can only be enabled by a comprehensive big data platform. Such a platform extends the Hadoop framework with built-in analytics, robust developer tools, and the integration, reliability, and security capabilities that enterprises demand for complex, large scale analytics. In this session, we will share innovative analytics use cases from actual customer implementations using an enterprise-class big data analytics platform.
Integrating Hadoop Into the Enterprise – Hadoop Summit 2012Jonathan Seidman
A look at common patterns being applied to leverage Hadoop with traditional data management systems and the emerging landscape of tools which provide access and analysis of Hadoop data with existing systems such as data warehouses, relational databases, and business intelligence tools.
Pivotal: Virtualize Big Data to Make the Elephant DanceEMC
Big Data and virtualization are two of the hottest trends in the industry today, yet the full potential for bringing the two together has not been fully realized. In this session, learn how virtualization brings the advantages of greater elasticity, stronger isolation for multi-tenancy, and a click HA protection to Hadoop, while maintaining the comparable performance to Hadoop on physical machines.
Objective 1: Understand the benefits of virtualizing Hadoop.
After this session you will be able to:
Objective 2: Understand how to get started with Pivotal HD Hadoop .
Objective 3: Understand where to find more information.
Database Development: The Object-oriented and Test-driven WayTechWell
As developers, we've created heuristics that help us build robust systems and employed test-driven development (TDD) to improve code design and counter instability. Yet object-oriented development principles and TDD have failed to gain traction in the database world. That’s because database development involves an additional driving force-the data. Max Guernsey shows how to treat databases as objects with classes of their own-rather than as containers of objects-and how to drive database designs from tests. He illustrates a way to give these database classes the ability to upgrade old data without introducing undue risk. Max also shares how to apply good object-oriented design principles to database classes and how to enforce semantic connections between databases and clients. Max demonstrates how it all works together, ensuring that your production databases work exactly the same as test databases, minimizing the risk of design changes, and enabling client applications to more easily keep up with database changes.
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing ItAleksandr Yampolskiy
Cinchcast (aka BlogTalkRadio) is a startup in New York City.
Using only a phone, you can broadcast your message globally to millions of listeners.
Thousands of broadcasts are happening every day on topics ranging from technology to battling cancer.
In this talk, we will discuss how we accomplished this, the technology behind it, and the challenges ahead.
We will talk about what it's like building a startup in .NET and the techniques we have used to scale, such as
HTML and donut caching, lazy loading of data, elastic search, as well as marrying telephony to the web stack.
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges.
این دیتابیس توسط Neo Technology در سال ۲۰۰۷ ایجاد شد و به صورت Opensource در اختیار کاربران قرار گرفت. آخرین نسخه Stable، ورژن ۳.۱ هست.
Accelerating big data with ioMemory and Cisco UCS and NOSQLSumeet Bansal
When great companies work together, an even greater outcome is possible. I am presenting this at the Oracle Open World 2012 at the Cisco theatre. Could one possibly support a twitter-like workload with just one server and few iodrives? Its all here.
Forefront 2010 Unified Access Gateway with SharePoint 2010 takes considerable planning and considerations depending on your topology. Here are a few things to note about it, and at least one way to do it.
Hadoop, SQL & NoSQL: No Longer an Either-or QuestionTony Baer
It used to be black and white. If you needed MapReduce processing, you chose Hadoop; if you needed standard query and reporting, you chose a SQL data warehouse. The decision is no longer clear cut. With YARN clearing the way for Hadoop to accept multiple workloads, Hadoop is no longer your father’s MapReduce machine – as frameworks are rapidly emerging for interactive SQL, search, streaming and other workloads. We are on the path toward a federated world of analytic and operational decision stores, but as the boundaries between platform types grow fuzzier, deciding what platforms to use and where to run which workloads grow trickier.
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
More Related Content
Similar to Faster Cheaper Better-Replacing Oracle with Hadoop & Solr
Hadoop Summit 2012 | Integrating Hadoop Into the EnterpriseCloudera, Inc.
The power of Hadoop lies in its ability to help users cost effectively analyze all kinds of data. We are now seeing the emergence of a new class of analytic applications that can only be enabled by a comprehensive big data platform. Such a platform extends the Hadoop framework with built-in analytics, robust developer tools, and the integration, reliability, and security capabilities that enterprises demand for complex, large scale analytics. In this session, we will share innovative analytics use cases from actual customer implementations using an enterprise-class big data analytics platform.
Integrating Hadoop Into the Enterprise – Hadoop Summit 2012Jonathan Seidman
A look at common patterns being applied to leverage Hadoop with traditional data management systems and the emerging landscape of tools which provide access and analysis of Hadoop data with existing systems such as data warehouses, relational databases, and business intelligence tools.
Pivotal: Virtualize Big Data to Make the Elephant DanceEMC
Big Data and virtualization are two of the hottest trends in the industry today, yet the full potential for bringing the two together has not been fully realized. In this session, learn how virtualization brings the advantages of greater elasticity, stronger isolation for multi-tenancy, and a click HA protection to Hadoop, while maintaining the comparable performance to Hadoop on physical machines.
Objective 1: Understand the benefits of virtualizing Hadoop.
After this session you will be able to:
Objective 2: Understand how to get started with Pivotal HD Hadoop .
Objective 3: Understand where to find more information.
Database Development: The Object-oriented and Test-driven WayTechWell
As developers, we've created heuristics that help us build robust systems and employed test-driven development (TDD) to improve code design and counter instability. Yet object-oriented development principles and TDD have failed to gain traction in the database world. That’s because database development involves an additional driving force-the data. Max Guernsey shows how to treat databases as objects with classes of their own-rather than as containers of objects-and how to drive database designs from tests. He illustrates a way to give these database classes the ability to upgrade old data without introducing undue risk. Max also shares how to apply good object-oriented design principles to database classes and how to enforce semantic connections between databases and clients. Max demonstrates how it all works together, ensuring that your production databases work exactly the same as test databases, minimizing the risk of design changes, and enabling client applications to more easily keep up with database changes.
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing ItAleksandr Yampolskiy
Cinchcast (aka BlogTalkRadio) is a startup in New York City.
Using only a phone, you can broadcast your message globally to millions of listeners.
Thousands of broadcasts are happening every day on topics ranging from technology to battling cancer.
In this talk, we will discuss how we accomplished this, the technology behind it, and the challenges ahead.
We will talk about what it's like building a startup in .NET and the techniques we have used to scale, such as
HTML and donut caching, lazy loading of data, elastic search, as well as marrying telephony to the web stack.
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges.
این دیتابیس توسط Neo Technology در سال ۲۰۰۷ ایجاد شد و به صورت Opensource در اختیار کاربران قرار گرفت. آخرین نسخه Stable، ورژن ۳.۱ هست.
Accelerating big data with ioMemory and Cisco UCS and NOSQLSumeet Bansal
When great companies work together, an even greater outcome is possible. I am presenting this at the Oracle Open World 2012 at the Cisco theatre. Could one possibly support a twitter-like workload with just one server and few iodrives? Its all here.
Forefront 2010 Unified Access Gateway with SharePoint 2010 takes considerable planning and considerations depending on your topology. Here are a few things to note about it, and at least one way to do it.
Hadoop, SQL & NoSQL: No Longer an Either-or QuestionTony Baer
It used to be black and white. If you needed MapReduce processing, you chose Hadoop; if you needed standard query and reporting, you chose a SQL data warehouse. The decision is no longer clear cut. With YARN clearing the way for Hadoop to accept multiple workloads, Hadoop is no longer your father’s MapReduce machine – as frameworks are rapidly emerging for interactive SQL, search, streaming and other workloads. We are on the path toward a federated world of analytic and operational decision stores, but as the boundaries between platform types grow fuzzier, deciding what platforms to use and where to run which workloads grow trickier.
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber.
Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable.
At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads.
At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
Faster Cheaper Better-Replacing Oracle with Hadoop & Solr
1. 1
Faster, cheaper, better
Replacing Oracle with
Hadoop and Solr
Ken Krugler
Scale Unlimited
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
2. 2
Obligatory Background
Ken Krugler - direct from Nevada City, California
Krugle startup (2005-2008) used Nutch, Hadoop, Solr
Now running Scale Unlimited
big data + search
consulting + training
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
3. 3
The 50,000ft View
We helped our client kick the RDBMS habit
It’s an analytics web site for display advertising
Got rid of DBs handling queries for their web site
Now uses Hadoop + Solr to...
cut costs
add features
improve performance
increase scalability
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
4. 4
What’s an Analytics Web Site?
Let the user ask questions about data
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
5. 5
Including Sexy Dashboards
All driven by slices of the data
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
6. 6
Behind the web site curtain
Each view or constraint change triggers queries
“sum ad impact for all advertisers on all networks, sort by sum, limit 10”
“sum ad impact by ad type for advertiser ‘oracle.com’”
For millions of records, you have to chose...
Fast, accurate, inexpensive - pick any two
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
7. 7
Combinatorial Explosion
Too many possibilities to pre-calculate everything
more than 10^5 publishers
more than 10^6 advertisers
30 ad networks, 3 day ranges, etc
So many trillions of possible combinations
Caching of DB query results isn’t very useful
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
8. 8
Trouble in UI Land
UI refresh took 10-30 seconds
Well outside of target range of “about a second or so”
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
9. 8
Trouble in UI Land
UI refresh took 10-30 seconds
Well outside of target range of “about a second or so”
0.1 second: instantaneous
1.0 second: I’m still in the flow
10 seconds: I’m bored
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
10. 9
Trouble in the back office
Beefy hardware for multiple DBs was expensive
AWS monthly cost approaching 5 figures
And the data sets needed to grow significantly
Constant schema changes meant painful data reloading
Extract, load, transform (inside of DB)
Re-indexing of DB fields
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
11. 10
A New Approach
Do analytics off-line using Hadoop
Pre-generate as much as possible
Use Solr as a NoSQL database
And leverage search, faceting
+ =
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
12. 11
Obligatory Architectural Slide
Two search servers
8 shards per index
Optimize response time
Additional indexes
autocompletion, etc.
200M total documents
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
13. 12
What Solr Gives Us
Fast, memory-efficient queries
Count the number of documents that match a query
Sort results by fields
And search - “Find all Flash ads with the word ‘diet’”
Fast faceting
Count # of results from query that have different values for a field
“How many different image ad sizes (w/counts) are used by google?”
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
14. 13
How to Connect the Dots
We have web crawl data - ads, advertisers, publishers, networks
http://www.michiguide.com/some-page.html text google
DIRECTV® For Businesses Save $13/mo ww.directv.com/business
We have target Solr schemas with the fields defined
<field name="network" type="string" indexed="true" stored="false" required="true" />
<field name="publisher" type="string" indexed="true" stored="false" required="true" />
How do we get from A to B?
Data
f(data)??? Index
Sources
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
15. 14
Hadoop ETL
Implement appropriate Extract, Transform, Load
Extract is just parsing text files that are stored in Amazon’s S3
Load is building the Solr index and deploying it to the search servers
What about that pesky “Transform” part?
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
16. 15
Simplicity Itself
25 Hadoop Jobs
Developed with Cascading
Daily run is $25
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
17. 16
Workflow Essentials
“Do analytics offline” means anything that involves aggregation
Solr is fine for first/last/count
Pre-calculate anything that does math on each record
Essentially index is pre-calculated answers to 200M questions
“what is trendline for ad impact of this advertiser on that publisher?”
“which ads use 300x250 images?”
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
18. 17
Combinatorial Explosion
Limit questions that can be asked
E.g. no arbitrary date ranges
Requires tricky “biggest bang for buck” decisions
Collapse entries that are “all” and only one other
Leverage Solr multi-value field support
network:all and network:doubleclick are one entry
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
19. 18
Reduce Duplicated Data
De-normalized schema means multiple records with similar data
“ad X on network Y”, “ad X on network Z”
We couldn’t use Solr’s “join” support (not in 3.6, issues with shards)
Non-indexed duplicated data goes into “special” records
e.g. the records that have “all” for a field value
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
20. 19
Defer Workflow Optimizations
Frequently tempted to get tricky
But helicopter stunts lead to pain and suffering
Often complex ETL means running multiple jobs in parallel
So job timing/prioritization is more important
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
21. 20
Analyzing Workflows
Sadly, hand analysis is
currently required
Key is no dead time
map/reduce slots
New solutions
Ambrose
Driven
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
22. 21
Useful Optimizations
“Cache” results - HDFS storage is cheap
Daily processing
Daily state + delta from today
Throw away data ASAP - avoid data baggage
Analytics data sets often have many, many fields
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
23. 22
Map-side Reduction
Reduce the amount of data being sent from map to reduce
Often is bottleneck for jobs, due to network overhead
Examples include aggregation, group-level filtering
Hadoop has “combiners”, which are post-map reducers
Do incremental reduce on map side before sending to reducers
Cascading has “AggregateBy”, which are in-map reducers
Keeps some number of results in memory using LRU queue
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
24. 23
Avoid Heuristics in Hadoop
What’s easy to describe (and implement) in a function...
is often painful and slow in map-reduce
Conditional/branching logic is common example
If this join result matches X, use it; otherwise join with Y and do Z
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
25. 24
The Net-Net
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
26. 24
The Net-Net
If you have a web site that provides analytics
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
27. 24
The Net-Net
If you have a web site that provides analytics
And it’s currently using a RDBMS like Oracle
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
28. 24
The Net-Net
If you have a web site that provides analytics
And it’s currently using a RDBMS like Oracle
You should be able to make it faster, cheaper, better (and scalable)
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
29. 24
The Net-Net
If you have a web site that provides analytics
And it’s currently using a RDBMS like Oracle
You should be able to make it faster, cheaper, better (and scalable)
Using Hadoop & Solr
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12
30. 25
Questions?
Feel free to contact me
http://www.scaleunlimited.com/contact/
Check out Lucid’s “Big Data & Solr” class
http://www.lucidimagination.com/services/training/
Check out Cascading
http://www.cascading.org/
Copyright (c) 2012 Scale Unlimited. All Rights Reserved.
Monday, June 11, 12