3 Things to Learn About:
*How Apache Kudu enables users to do more than ever before with their Analytic and Operational Databases
*How Cloudera has built two versatile databases to help our customers tackle their hardest problems.
*How the addition of Apache Kudu to this mix will enable new use cases around real-time analytics, internet of things, time series data, and more.
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Delta Lake is an open source storage layer that sits on top of data lakes and brings ACID transactions and reliability to Apache Spark. It addresses challenges with data lakes like lack of schema enforcement and transactions. Delta Lake provides features like ACID transactions, scalable metadata handling, schema enforcement and evolution, time travel/data versioning, and unified batch and streaming processing. Delta Lake stores data in Apache Parquet format and uses a transaction log to track changes and ensure consistency even for large datasets. It allows for updates, deletes, and merges while enforcing schemas during writes.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Moving Beyond Lambda Architectures with Apache KuduCloudera, Inc.
The document discusses the Lambda architecture, its advantages and disadvantages, and how Kudu can serve as an alternative. The Lambda architecture marries batch and real-time processing by using separate batch, speed, and serving layers. While it provides scalability, maintaining two code bases is complex. Kudu can fill the gap by enabling both fast analytics on frequently updated data through its ability to support updates, scans and lookups simultaneously. Examples of how Kudu has been used by Xiaomi to simplify their analytics pipeline and reduce latency are provided. The document cautions against premature optimization and advocates optimizing only as needed.
How to build a streaming Lakehouse with Flink, Kafka, and HudiFlink Forward
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
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Hive Bucketing in Apache Spark with Tejas PatilDatabricks
Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling.
In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and ...Databricks
Uber has real needs to provide faster, fresher data to data consumers & products, running hundreds of thousands of analytical queries everyday. Uber engineers will share the design, architecture & use-cases of the second generation of ‘Hudi’, a self contained Apache Spark library to build large scale analytical datasets designed to serve such needs and beyond. Hudi (formerly Hoodie) is created to effectively manage petabytes of analytical data on distributed storage, while supporting fast ingestion & queries. In this talk, we will discuss how we leveraged Spark as a general purpose distributed execution engine to build Hudi, detailing tradeoffs & operational experience. We will also show to ingest data into Hudi using Spark Datasource/Streaming APIs and build Notebooks/Dashboards on top using Spark SQL.
Some Iceberg Basics for Beginners (CDP).pdfMichael Kogan
The document describes the recommended Iceberg workflow which includes 8 steps:
1) Create Iceberg tables from existing datasets or sample datasets
2) Batch insert data to prepare for time travel scenarios
3) Create security policies for fine-grained access control
4) Build BI queries for reporting
5) Build visualizations from query results
6) Perform time travel queries to audit changes
7) Optimize partition schemas to improve query performance
8) Manage and expire snapshots for table maintenance
Doug Bateman, a principal data engineering instructor at Databricks, presented on how to build a Lakehouse architecture. He began by introducing himself and his background. He then discussed the goals of describing key Lakehouse features, explaining how Delta Lake enables it, and developing a sample Lakehouse using Databricks. The key aspects of a Lakehouse are that it supports diverse data types and workloads while enabling using BI tools directly on source data. Delta Lake provides reliability, consistency, and performance through its ACID transactions, automatic file consolidation, and integration with Spark. Bateman concluded with a demo of creating a Lakehouse.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Learn how Apache Atlas is being enhanced to provide a universal open metadata and governance platform for all data processing across the enterprise. With open metadata, multiple metadata repositories, potentially from different vendors, can operate collaboratively to create an enterprise catalog of data that can be located, understood, used and governed. In this talk we will provide a detailed description of the extensions to the type system, new APIs, the connector framework, metadata discovery framework, governance action framework and the inter-operability that we are adding to Apache Atlas. We will show examples of these features in operation. For example, (1) how metadata is discovered and gathered into Apache Atlas, (2) how applications and tools access metadata, (3) how enforcement engines such as Apache Ranger keep synchronized with the latest governance requirements and (4) how to build an adapter to allow other vendor's metadata repositories can exchange metadata with Apache Atlas repositories. We will also explain how these features can be deployed together to support the Hadoop platform, and the enterprise beyond. This session will be presented by Nigel Jones - IBM & Ferd Schapers - ING Chief Information Architect
Speaker:
Nigel Jones, Software Architect, IBM Analytics Group, IBM
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Introduction SQL Analytics on Lakehouse ArchitectureDatabricks
This document provides an introduction and overview of SQL Analytics on Lakehouse Architecture. It discusses the instructor Doug Bateman's background and experience. The course goals are outlined as describing key features of a data Lakehouse, explaining how Delta Lake enables a Lakehouse architecture, and defining features of the Databricks SQL Analytics user interface. The course agenda is then presented, covering topics on Lakehouse Architecture, Delta Lake, and a Databricks SQL Analytics demo. Background is also provided on Lakehouse architecture, how it combines the benefits of data warehouses and data lakes, and its key features.
The columnar roadmap: Apache Parquet and Apache ArrowJulien Le Dem
This document discusses Apache Parquet and Apache Arrow, open source projects for columnar data formats. Parquet is an on-disk columnar format that optimizes I/O performance through compression and projection pushdown. Arrow is an in-memory columnar format that maximizes CPU efficiency through vectorized processing and SIMD. It aims to serve as a standard in-memory format between systems. The document outlines how Arrow builds on Parquet's success and provides benefits like reduced serialization overhead and ability to share functionality through its ecosystem. It also describes how Parquet and Arrow representations are integrated through techniques like vectorized reading and predicate pushdown.
Using Apache Arrow, Calcite, and Parquet to Build a Relational CacheDremio Corporation
From DataEngConf 2017 - Everybody wants to get to data faster. As we move from more general solution to specific optimization techniques, the level of performance impact grows. This talk will discuss how layering in-memory caching, columnar storage and relational caching can combine to provide a substantial improvement in overall data science and analytical workloads. It will include a detailed overview of how you can use Apache Arrow, Calcite and Parquet to achieve multiple magnitudes improvement in performance over what is currently possible.
Part 1: Cloudera’s Analytic Database: BI & SQL Analytics in a Hybrid Cloud WorldCloudera, Inc.
3 Things to Learn About:
* On-premises versus the cloud: What’s the same and what’s different?
* Design and benefits of analytics in the cloud
* Best practices and architectural considerations
Part 2: Cloudera’s Operational Database: Unlocking New Benefits in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud
*Design & benefits of real-time operational data in the cloud
*Best practices and architectural considerations
Data Engineering: Elastic, Low-Cost Data Processing in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud: What’s the same and what’s different?
*Benefits of data processing in the cloud
*Best practices and architectural considerations
Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.
Gartner Data and Analytics Summit: Bringing Self-Service BI & SQL Analytics ...Cloudera, Inc.
For self-service BI and exploratory analytic workloads, the cloud can provide a number of key benefits, but the move to the cloud isn’t all-or-nothing. Gartner predicts nearly 80 percent of businesses will adopt a hybrid strategy. Learn how a modern analytic database can power your business-critical workloads across multi-cloud and hybrid environments, while maintaining data portability. We'll also discuss how to best leverage the increased agility cloud provides, while maintaining peak performance.
This document discusses using Cloudera Enterprise to analyze data from connected cars. It begins with an overview of the connected car market and use cases such as predictive maintenance, usage-based insurance, and mobility management. Examples are given of how major automakers and insurance companies are using connected car data and analytics. The rest of the document focuses on Cloudera Enterprise's capabilities for ingesting, storing, processing, and analyzing large volumes of diverse connected car data in real-time and batch modes. A demo is outlined to showcase predictive maintenance, usage-based insurance, and public services use cases.
The document discusses how Sparklyr allows data scientists to access and work with data stored in Cloudera Enterprise using the popular RStudio IDE. It describes the challenges data scientists face in accessing secured Hadoop clusters and limitations of notebook environments. Sparklyr integration with RStudio provides a familiar environment for data scientists to access Hadoop data and compute using Spark, enabling distributed data science workflows directly in R. The presentation demonstrates how to analyze over a billion records using Spark and R through Sparklyr.
Using Big Data to Transform Your Customer’s Experience - Part 1 Cloudera, Inc.
3 Things to Learn About:
-How the Customer Insights Solution helped
- How customer insights can improve customer loyalty, reduce customer churn, and increase upsell opportunities
- Which real-world use cases are ideal for using big data analytics on customer data
3 Things to Learn About:
*The IoT ecosystem and data management considerations for IoT
*Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data
*Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data
Kudu is a storage engine for Hadoop designed to address gaps in Hadoop's ability to handle workloads that require both high-throughput data ingestion and low-latency random access. It is a columnar storage engine that uses a log-structured merge tree to store data and provides APIs for NoSQL and SQL access. Kudu aims to provide high performance for both scans and random access through its columnar design and tablet architecture that partitions data across servers.
Kudu is an open source storage layer developed by Cloudera that provides low latency queries on large datasets. It uses a columnar storage format for fast scans and an embedded B-tree index for fast random access. Kudu tables are partitioned into tablets that are distributed and replicated across a cluster. The Raft consensus algorithm ensures consistency during replication. Kudu is suitable for applications requiring real-time analytics on streaming data and time-series queries across large datasets.
3 Things to Learn About:
-How Kudu is able to fill the analytic gap between HDFS and Apache HBase
-The trade-offs between real-time transactional access and fast analytic performance
-How Kudu provides an option to achieve fast scans and random access from a single API
3 Things to Learn:
-How data is driving digital transformation to help businesses innovate rapidly
-How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business
-How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements
How Big Data Can Enable Analytics from the Cloud (Technical Workshop)Cloudera, Inc.
In this workshop, we will look outside the box and help expand the problem space to include issues you may not have thought were possible before Big Data. From Near Real Time (NRT) recommendation engines, loan applications to churn detection, Big Data is answering new questions and providing organisations with a competitive edge through revenue increase, cost savings and risk mitigation. We will take a special look at the role the Cloud can play in elevating your analytics environment. We will discuss real world examples of how Big Data answers these questions and does it at a lower cost outlay.
Securing the Data Hub--Protecting your Customer IP (Technical Workshop)Cloudera, Inc.
Your data is your IP and its security is paramount. The last thing you want is for your data to become a target for threats. This workshop will focus on the realities of protecting your customer’s IP from external and internal threats with battle hardened technologies and methodologies. Another key concept that will be examined is the connection of people, processes and technology. In addition, the session will take a look at authentication and authorisation, auditing and data lineage as well as the different groups required to play a part in the modern data hub. We will also look at how to produce high impact operation reports from Cloudera’s RecordService a new core security layer that centrally enforces fine-grained access control policy, which helps close the feedback loop to ensure awareness of security as a living entity within your organisation.
Building a Data Hub that Empowers Customer Insight (Technical Workshop)Cloudera, Inc.
We have seen the evolution with the Bi and Data Science fields from the structured data warehouse to data lake and finally, to the data hub. This session will cover the key steps required to building a data hub, examining how best to align and engage stakeholders and develop architectural sanction to enable your organisations to realise new customer insights and better enable you to achieve business objectives.
The document outlines topics covered in "The Impala Cookbook" published by Cloudera. It discusses physical and schema design best practices for Impala, including recommendations for data types, partition design, file formats, and block size. It also covers estimating and managing Impala's memory usage, and how to identify the cause when queries exceed memory limits.
The Vortex of Change - Digital Transformation (Presented by Intel)Cloudera, Inc.
The vortex of change continues all around us – inside the company, with our customers and partners. A new norm is upon us. Business models are being turned upside down – the hunters now the hunted, global equalization – size is no longer a guarantee of success. The innovative survive and thrive…the nervous and slow go under...what does all this change means for you? Find out how does Intel’s strengths help our customers in this world of change.
Reactive Fast Data & the Data Lake with Akka, Kafka, SparkTodd Fritz
This document discusses a presentation titled "Reactive Fast Data & the Data Lake with Akka, Kafka, Spark" given by Todd Fritz at DevNexus in February 2017. The presentation agenda covers reactive systems and patterns, fast data, data lakes, the intersection of these topics, and architecture considerations for building systems that can scale to millions of users and billions of messages. Key technologies discussed include Akka, Kafka, and Spark.
Streaming all the things with akka streams Johan Andrén
This document provides an overview and introduction to Akka Streams and Reactive Streams. Some key points:
- Reactive Streams is a standard for asynchronous stream processing with non-blocking back pressure to prevent issues like out of memory errors.
- Akka Streams is a toolkit for building powerful concurrent and distributed applications simply using a Reactive Streams-compliant API. It includes sources, sinks, flows and other stages for stream processing.
- Examples show how to create simple stream graphs that process data asynchronously using Akka Streams APIs in both Java and Scala in just a few lines of code. More complex examples demonstrate features like parallelization.
- The community Alpakka
Simplifying Real-Time Architectures for IoT with Apache KuduCloudera, Inc.
3 Things to Learn About:
*Building scalable real time architectures for managing data from IoT
*Processing data in real time with components such as Kudu & Spark
*Customer case studies highlighting real-time IoT use cases
Enabling the Active Data Warehouse with Apache KuduGrant Henke
Apache Kudu is an open source data storage engine that makes fast analytics on fast and changing data easy. In this presentation, Grant Henke from Cloudera will provide an overview of what Kudu is, how it works, and how it makes building an active data warehouse for real time analytics easy. Drawing on experiences from some of our largest deployments, this talk will also include an overview of common Kudu use cases and patterns. Additionally, some of the newest Kudu features and what is coming next will be covered.
This document discusses Cloudera's initiative to make Spark the standard execution engine for Hadoop. It outlines how Spark improves on MapReduce by leveraging distributed memory and having a simpler developer experience. It also describes Cloudera's investments in areas like management, security, scale, and streaming to further Spark's capabilities and make it production-ready. The goal is for Spark to replace MapReduce as the execution engine and for specialized engines like Impala to handle specific workloads, with all sharing the same data, metadata, resource management, and other platform services.
Big data journey to the cloud 5.30.18 asher bartchCloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
Cloudera can help optimize Splunk deployments by providing more cost-effective scalability, increased data flexibility, and enhanced analytics capabilities. Cloudera can ingest data from Splunk indexes and apply enrichment using open-source machine learning before storing the data in its data hub. This provides a single platform for advanced analytics like SQL and Python/R scripts across both historical and new data. Initial use cases include offloading event data from Splunk to reduce costs and loading additional context sources to gain better insights.
Unlock Hadoop Success with Cloudera Navigator OptimizerCloudera, Inc.
Cloudera Navigator Optimizer analyzes existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop.
Cassandra Summit 2014: Internet of Complex Things Analytics with Apache Cassa...DataStax Academy
Speaker: Mohammed Guller, Application Architect & Lead Developer at Glassbeam.
Learn how Cassandra can be used to build a multi-tenant solution for analyzing operational data from Internet of Complex Things (IoCT). IoCT includes complex systems such as computing, storage, networking and medical devices. In this session, we will discuss why Glassbeam migrated from a traditional RDBMS-based architecture to a Cassandra-based architecture. We will discuss the challenges with our first-generation architecture and how Cassandra helped us overcome those challenges. In addition, we will share our next-gen architecture and lessons learned.
Building a Pluggable Analytics Stack with Cassandra (Jim Peregord, Element Co...DataStax
Element Fleet has the largest benchmark database in our industry and we needed a robust and linearly scalable platform to turn this data into actionable insights for our customers. The platform needed to support advanced analytics, streaming data sets, and traditional business intelligence use cases.
In this presentation, we will discuss how we built a single, unified platform for both Advanced Analytics and traditional Business Intelligence using Cassandra on DSE. With Cassandra as our foundation, we are able to plug in the appropriate technology to meet varied use cases. The platform we’ve built supports real-time streaming (Spark Streaming/Kafka), batch and streaming analytics (PySpark, Spark Streaming), and traditional BI/data warehousing (C*/FiloDB). In this talk, we are going to explore the entire tech stack and the challenges we faced trying support the above use cases. We will specifically discuss how we ingest and analyze IoT (vehicle telematics data) in real-time and batch, combine data from multiple data sources into to single data model, and support standardized and ah-hoc reporting requirements.
About the Speaker
Jim Peregord Vice President - Analytics, Business Intelligence, Data Management, Element Corp.
Feature Store as a Data Foundation for Machine LearningProvectus
This document discusses feature stores and their role in modern machine learning infrastructure. It begins with an introduction and agenda. It then covers challenges with modern data platforms and emerging architectural shifts towards things like data meshes and feature stores. The remainder discusses what a feature store is, reference architectures, and recommendations for adopting feature stores including leveraging existing AWS services for storage, catalog, query, and more.
The document discusses Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Kudu is designed to fill the gap between HDFS and HBase by providing fast analytics capabilities on fast-changing or frequently updated data. It achieves this through its scalable and fast tabular storage design that allows for both high insert/update throughput and fast scans/queries. The document provides an overview of Kudu's architecture and capabilities, examples of how to use its NoSQL and SQL APIs, and real-world use cases like enabling low-latency analytics pipelines for companies like Xiaomi.
This deck covers key considerations and provides advice for enterprises looking to run production-scale Cloudera on AWS. We touch on everything from security to governance to selecting the right instance type for your Hadoop workload (Spark, Impala, Search, etc).
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
Cloudera Altus: Big Data in der Cloud einfach gemachtCloudera, Inc.
Neueste Studien zeigen, dass Data Scientisten und Analysten bis zu 80% ihrer Zeit dafür nutzen, Daten zu reinigen und vorzubereiten.
Eine ohnehin schon zeitaufwändige Aufgabe kann in der Cloud noch weiter erschwert werden, da das Cluster Management und Operations die Komplexität noch erhöhen.
Nutzer wünschen sich daher, diese komplexen Workflows zu vereinheitlichen und zu vereinfachen.
Um Big Data und Machine Learning Initiativen voranzutreiben, benötigen Unternehmen eine skalierbare und überall verfügbare Plattform. Diese muss Self-Service ermöglichen und Datensilos eliminieren.
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
Large-Scale Data Science on Hadoop (Intel Big Data Day)Uri Laserson
The document discusses data science workflows on Hadoop. It describes data science as involving three phases - data plumbing to ingest and transform data, exploratory analytics to investigate and analyze data, and operational analytics to build and deploy models. It provides examples of tools used for each phase including Spark, Hadoop streaming, SAS, and Python for exploratory analytics, and MLlib and Spark for operational analytics. The document also discusses lambda architectures for handling both batch and real-time analytics.
HiFX designed and implemented a unified data analytics platform called Vision Lens for Malayala Manorama to generate meaningful insights from large amounts of data across their multiple digital properties. The solution involved building a data lake, data pipeline, processing framework, and dashboards to provide real-time and historical analytics. This helped Manorama improve user experiences, drive smarter marketing, and make better business decisions.
Cloudera Analytics and Machine Learning Platform - Optimized for Cloud Stefan Lipp
Take Data Management to the next level: Connect Analytics and Machine Learning in a single governed platform consisting of a curated protable open source stack. Run this platform on-prem, hybrid or multicloud, reuse code and models avoid lock-in.
Similar to Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic Databases (20)
The document discusses using Cloudera DataFlow to address challenges with collecting, processing, and analyzing log data across many systems and devices. It provides an example use case of logging modernization to reduce costs and enable security solutions by filtering noise from logs. The presentation shows how DataFlow can extract relevant events from large volumes of raw log data and normalize the data to make security threats and anomalies easier to detect across many machines.
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
The document outlines the 2021 finalists for the annual Data Impact Awards program, which recognizes organizations using Cloudera's platform and the impactful applications they have developed. It provides details on the challenges, solutions, and outcomes for each finalist project in the categories of Data Lifecycle Connection, Cloud Innovation, Data for Enterprise AI, Security & Governance Leadership, Industry Transformation, People First, and Data for Good. There are multiple finalists highlighted in each category demonstrating innovative uses of data and analytics.
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
The document outlines the agenda for Cloudera's Enterprise Data Cloud event in Vienna. It includes welcome remarks, keynotes on Cloudera's vision and customer success stories. There will be presentations on the new Cloudera Data Platform and customer case studies, followed by closing remarks. The schedule includes sessions on Cloudera's approach to data warehousing, machine learning, streaming and multi-cloud capabilities.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
The document discusses the benefits and trends of modernizing a data warehouse. It outlines how a modern data warehouse can provide deeper business insights at extreme speed and scale while controlling resources and costs. Examples are provided of companies that have improved fraud detection, customer retention, and machine performance by implementing a modern data warehouse that can handle large volumes and varieties of data from many sources.
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
Predicting Test Results without Execution (FSE 2024)Andre Hora
As software systems grow, test suites may become complex, making it challenging to run the tests frequently and locally. Recently, Large Language Models (LLMs) have been adopted in multiple software engineering tasks. It has demonstrated great results in code generation, however, it is not yet clear whether these models understand code execution. Particularly, it is unclear whether LLMs can be used to predict test results, and, potentially, overcome the issues of running real-world tests. To shed some light on this problem, in this paper, we explore the capability of LLMs to predict test results without execution. We evaluate the performance of the state-of-the-art GPT-4 in predicting the execution of 200 test cases of the Python Standard Library. Among these 200 test cases, 100 are passing and 100 are failing ones. Overall, we find that GPT-4 has a precision of 88.8%, recall of 71%, and accuracy of 81% in the test result prediction. However, the results vary depending on the test complexity: GPT-4 presented better precision and recall when predicting simpler tests (93.2% and 82%) than complex ones (83.3% and 60%). We also find differences among the analyzed test suites, with the precision ranging from 77.8% to 94.7% and recall between 60% and 90%. Our findings suggest that GPT-4 still needs significant progress in predicting test results.
In today's dynamic business landscape, ERP software systems are essential tools for businesses worldwide, including those in the UAE. These systems cater to the unique needs of the UAE's rapidly changing economy and expanding industries.
This blog examines the top 10 ERP companies in the UAE, highlighting their innovative products, exceptional customer support, and significant impact on the regional business community. These companies excel in providing ERP solutions that enhance efficiency and growth for businesses throughout the UAE.
1. **Odoo**
- Odoo ERP is a comprehensive business management solution with features like accounting, HR, sales, inventory control, and CRM. Its user-friendly interface simplifies processes and boosts productivity. Banibro IT Solutions leverages Odoo to transform business operations.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Open Source: Yes
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Chat, Email
- Payment: Yearly, Monthly
- Multi-Language: Yes
- OS Support: Windows, Mac, iOS, Android
- API: Available
2. **Microsoft Dynamics 365**
- Dynamics 365 offers a centralized platform for small and medium-sized businesses, integrating with Microsoft apps and cloud services for scalability. It simplifies data processing with user-friendly interfaces and customizable reporting.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, iOS, Android
- API: Not specified
3. **FirstBIT ERP**
- Known for serving small and medium-sized businesses, FirstBIT ERP offers comprehensive solutions and exceptional customer service, enhancing productivity and efficiency.
- **Details:**
- Suitable for: Medium, Large Businesses
- Open Source: Yes/No
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Email, Video Tutorials
- Payment: Yearly, Monthly
- Multi-Language: Yes
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Available
4. **Ezware Technologies**
- Ezware Technologies provides top-notch ERP solutions for various industries with user-friendly modules that streamline complex business processes.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Not specified
5. **RealSoft**
- RealSoft by Coral is popular in Dubai, offering modules for contracting, real estate, job costing, manufacturing, trading, and finance. It's VAT-enabled and affordable for medium-sized businesses.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Open Source: No
- Cloud-based: On-premises
-
Bring Strategic Portfolio Management to Monday.com using OnePlan - Webinar 18...OnePlan Solutions
Unlock the full potential of your projects with OnePlan’s seamless integration with monday.com. Join us to discover how OnePlan enhances monday.com by aligning your portfolio of projects with your organization’s strategic goals, optimizing resource allocation, and streamlining performance tracking. Learn how this powerful combination can drive efficiency, cost savings, and strategic success within your organization.
Unlocking the Future of Artificial IntelligencedorinIonescu
Unlock the Future: Dive into AI Today! Videnda AI specializes in developing advanced artificial intelligence solutions, including visual dictionaries and language learning tools that leverage immersive virtual travel experiences. Stay Ahead of the Curve: Master AI Now! Our AI technology integrates machine learning and neural networks to enhance education and business applications. AI: The Next Frontier. Are You Ready to Explore? With a focus on real-time AI solutions and deep learning models, Videnda AI provides innovative tools for multilingual communication and immersive learning.
In this course, you'll find a series of engaging videos packed with vibrant animations that break down complex AI concepts into digestible pieces. Our curriculum covers AI models such as Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Generative Adversarial Networks (GAN), and Transformers, providing a solid understanding of these models and their real-world applications. We also offer hands-on experience with Generative AI tools like ChatGPT and Midjourney, and Python programming tutorials to help you implement AI algorithms and build your own AI applications.
We are proud participants in the Nvidia Inception Program, driving AI innovation across various industries. By the end of our course, you'll have a strong understanding of AI principles, enhanced Python programming skills, and practical experience with state-of-the-art Generative AI tools. Whether you're looking to kickstart a career in AI or simply curious about this revolutionary technology, Videnda AI is your partner in mastering the future of artificial intelligence.
Understanding Automated Testing Tools for Web Applications.pdfkalichargn70th171
Automated testing tools for web applications are revolutionizing how we ensure quality and performance in software development. These tools help save time, reduce human error, and increase the efficiency of web application testing processes. This guide delves into automated testing, discusses the available tools, and highlights how to choose the right tool for your needs.
Mastering MicroStation DGN: How to Integrate CAD and GISSafe Software
Dive deep into the world of CAD-GIS integration and elevate your workflows to nexl-level efficiency levels. Discover how to seamlessly transfer data between Bentley MicroStation and leading GIS platforms, such as Esri ArcGIS.
This session goes beyond mere CAD/GIS conversion, showcasing techniques to precisely transform MicroStation elements including cells, text, lines, and symbology. We’ll walk you through tags versus item types, and understanding how to leverage both. You’ll also learn how to reproject to any coordinate system. Finally, explore cutting-edge automated methods for managing database links, and delve into innovative strategies for enabling self-serve data collection and validation services.
Join us to overcome the common hurdles in CAD and GIS integration and enhance the efficiency of your workflows. This session is perfect for professionals, both new to FME and seasoned users, seeking to streamline their processes and leverage the full potential of their CAD and GIS systems.
BitLocker Data Recovery | BLR Tools Data Recovery SolutionsAlina Tait
BLR Tools provides an advanced BitLocker Data Recovery Tool specifically engineered to recover lost or inaccessible data from BitLocker-encrypted drives. Whether you're dealing with accidental deletion, encryption key problems, or system crashes, our cutting-edge software guarantees a secure and efficient recovery process. Rely on BLR Tools for dependable BitLocker data recovery and effortlessly restore access to your essential files.
Literals - A Machine Independent Feature21h16charis
Introduction to Literals, A machine independent feature. The presentation is based on the prescribed textbook for System Software and Compiler Design, Computer Science and Engineering - System Software by Leland. L. Beck,
D Manjula.
Alluxio Webinar | What’s new in Alluxio Enterprise AI 3.2: Leverage GPU Anywh...Alluxio, Inc.
Alluxio Webinar
July.23, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Shouwei Chen (core maintainer and product manager, Alluxio)
In today's AI-driven world, organizations face unprecedented demands for powerful AI infrastructure to fuel their model training and serving workloads. Performance bottlenecks, cost inefficiencies, and management complexities pose significant challenges for AI platform teams supporting large-scale model training and serving. On July 9, 2024, we introduced Alluxio Enterprise AI 3.2, a groundbreaking solution designed to address these critical issues in the ever-evolving AI landscape.
In this webinar, Shouwei Chen will introduce exciting new features of Alluxio Enterprise AI 3.2:
- Leveraging GPU resources anywhere accessing remote data with the same local performance
- Enhanced I/O performance with 97%+ GPU utilization for popular language model training benchmarks
- Achieving the same performance as HPC storage on existing data lake without additional HPC storage infrastructure
- New Python FileSystem API to seamlessly integrate with Python applications like Ray
- Other new features, include advanced cache management, rolling upgrades, and CSI failover
BDRSuite - #1 Cost effective Data Backup and Recovery Solutionpraveene26
BDRSuite and BDRCloud by Vembu are comprehensive and cost-effective backup and disaster recovery solutions designed to meet the diverse data protection requirements of Businesses and Service Providers.
With BDRSuite & BDRCloud, you can backup diverse IT workloads from any location, including VMs (VMware, Hyper-V, KVM, Proxmox VE, oVirt), Servers & Endpoints (Windows, Linux, Mac), SaaS Applications (Microsoft 365, Google Workspace), Cloud VMs (AWS, Azure), NAS/File Shares and Databases & Applications (Microsoft Exchange Server, SQL Server, SharePoint Server, PostgreSQL, MySQL).
You can store backup anywhere like On-Premise/Remote storage, Private/Public Cloud, and BDRCloud.
You can centrally manage the entire backup infrastructure with BDRSuite’s self-hosted centralized management console (or) BDRCloud-hosted centralized management console.
You can quickly recover from data loss or ransomware attacks—all at an affordable price.
To know more visit our website -
https://www.bdrsuite.com/
https://www.bdrcloud.com/