As disparate data volumes continue to be operationalized across the enterprise, data will need to be processed, cleansed, transformed, and made available to end users at greater speeds. Traditional ODS systems run into issues when trying to process large data volumes causing operations to be backed up, data to be archived, and ETL/ ELT processes to fail. Join this breakout to learn how to battle these issues.
Learn more about Talend Integration Cloud - http://www.talend.com/products/integration-cloud
Talend Integration Cloud includes the powerful Talend Studio and new web-based designer tools to maximize your productivity. Speed cloud integration using robust graphical tools and wizards inside Talend Integration Cloud. More than 900 connectors and components simplify development of cloud-to-cloud and hybrid integration flows to deploy as governed integration services. Build simple or complex integration flows inside Talend Studio that connect, cleanse, and transform data. Simply push a button to publish and go live in seconds. Easily de-duplicate and standardize data to increase information accuracy and completeness.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Building Event Streaming Architectures on Scylla and KafkaScyllaDB
Event streaming architectures require high-throughput, low-latency components to consistently and smoothly transfer data between heterogenous transactional and analytical systems. Join us and Confluent's Tim Berglund to learn how the Scylla and Confluent Kafka interoperate as a foundation upon which you can build enterprise-grade, event-driven applications, plus a use case from Numberly.
Performance Optimizations in Apache ImpalaCloudera, Inc.
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
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.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Struggling to keep up with an ever-increasing demand for data at your organisation? Do you spend hours tinkering with your streaming data pipelines? Does that one data scientist with direct EDW access keep you up at night? Introducing Snowflake, a brand new SQL data warehouse built for the cloud. We’ve designed and implemented a unique cloud-based architecture that addresses the most common shortcomings of existing data solutions. With Snowflake, you can unlock unlimited concurrency, enable instant scalability, and take advantage of built-in tuning and optimisation. Join us and find out what Netflix, Adobe, and Nike all have in common.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Learn more about Talend Integration Cloud - http://www.talend.com/products/integration-cloud
Talend Integration Cloud includes the powerful Talend Studio and new web-based designer tools to maximize your productivity. Speed cloud integration using robust graphical tools and wizards inside Talend Integration Cloud. More than 900 connectors and components simplify development of cloud-to-cloud and hybrid integration flows to deploy as governed integration services. Build simple or complex integration flows inside Talend Studio that connect, cleanse, and transform data. Simply push a button to publish and go live in seconds. Easily de-duplicate and standardize data to increase information accuracy and completeness.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Building Event Streaming Architectures on Scylla and KafkaScyllaDB
Event streaming architectures require high-throughput, low-latency components to consistently and smoothly transfer data between heterogenous transactional and analytical systems. Join us and Confluent's Tim Berglund to learn how the Scylla and Confluent Kafka interoperate as a foundation upon which you can build enterprise-grade, event-driven applications, plus a use case from Numberly.
Performance Optimizations in Apache ImpalaCloudera, Inc.
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
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.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Struggling to keep up with an ever-increasing demand for data at your organisation? Do you spend hours tinkering with your streaming data pipelines? Does that one data scientist with direct EDW access keep you up at night? Introducing Snowflake, a brand new SQL data warehouse built for the cloud. We’ve designed and implemented a unique cloud-based architecture that addresses the most common shortcomings of existing data solutions. With Snowflake, you can unlock unlimited concurrency, enable instant scalability, and take advantage of built-in tuning and optimisation. Join us and find out what Netflix, Adobe, and Nike all have in common.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Migrating your traditional Data Warehouse to a Modern Data LakeAmazon Web Services
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
SQL Analytics Powering Telemetry Analysis at ComcastDatabricks
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience.
In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses.
We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
Continuous Data Replication into Cloud Storage with Oracle GoldenGateMichael Rainey
Continuous flow. Streaming. Near real-time. These are all terms used to identify the business’s need for quick access to data. It’s a common request, even if the data must flow from on-premises to the cloud. Oracle GoldenGate is the data replication solution built for fast data. In this session, we’ll look at how GoldenGate can be configured to extract transactions from the Oracle database and load them into a cloud object store, such as Amazon S3. There are many different use cases for this type of continuous load of data into the cloud. We’ll explore these solutions and the various tools that can be used to access and analyze the data from the cloud object store, leaving attendees with ideas for implementing a full source-to-cloud data replication solution.
Presented at ITOUG Tech Days 2019
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
The Data Lake Engine Data Microservices in Spark using Apache Arrow FlightDatabricks
Machine learning pipelines are a hot topic at the moment. Moving data through the pipeline in an efficient and predictable way is one of the most important aspects of running machine learning models in production.
Lambda Architecture in the Cloud with Azure Databricks with Andrei VaranovichDatabricks
The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising.
In this talk we demonstrate the blueprint for such an implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component. We go back to some core principles of functional programming and link them to the capabilities of Apache Spark for various end-to-end big data analytics scenarios.
We also illustrate the “Lambda architecture in use” and the associated tread-offs using the real customer scenario – Rijksmuseum in Amsterdam – a terabyte-scale Azure-based data platform handles data from 2.500.000 visitors per year.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Introduction to failover clustering with sql serverEduardo Castro
In this presentation we review the basic requirements to install a SQL Server Failover Cluster.
Regards,
Eduardo Castro Martinez
http://ecastrom.blogspot.com
http://comunidadwindows.org
Modeling Data and Queries for Wide Column NoSQLScyllaDB
Discover how to model data for wide column databases such as ScyllaDB and Apache Cassandra. Contrast the differerence from traditional RDBMS data modeling, going from a normalized “schema first” design to a denormalized “query first” design. Plus how to use advanced features like secondary indexes and materialized views to use the same base table to get the answers you need.
Siloed data is difficult to access and causes data consumers to only have partial views of the problem at hand. By limiting access to large volumes of disparate data, analysts and business users alike don’t have the ability to included important data in their reports and models leading to suboptimal analytic outputs. Even when this data is available to countless users, traditional systems limit them to querying small volumes of data in order to return the results in a timely matter.
Migrating your traditional Data Warehouse to a Modern Data LakeAmazon Web Services
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
SQL Analytics Powering Telemetry Analysis at ComcastDatabricks
Comcast is one of the leading providers of communications, entertainment, and cable products and services. At the heart of it is Comcast RDK providing the backbone of telemetry to the industry. RDK (Reference Design Kit) is pre-bundled opensource firmware for a complete home platform covering video, broadband and IoT devices. RDK team at Comcast analyzes petabytes of data, collected every 15 minutes from 70 million devices (video and broadband and IoT devices) installed in customer homes. They run ETL and aggregation pipelines and publish analytical dashboards on a daily basis to reduce customer calls and firmware rollout. The analysis is also used to calculate WIFI happiness index which is a critical KPI for Comcast customer experience.
In addition to this, RDK team also does release tracking by analyzing the RDK firmware quality. SQL Analytics allows customers to operate a lakehouse architecture that provides data warehousing performance at data lake economics for up to 4x better price/performance for SQL workloads than traditional cloud data warehouses.
We present the results of the “Test and Learn” with SQL Analytics and the delta engine that we worked in partnership with the Databricks team. We present a quick demo introducing the SQL native interface, the challenges we faced with migration, The results of the execution and our journey of productionizing this at scale.
Continuous Data Replication into Cloud Storage with Oracle GoldenGateMichael Rainey
Continuous flow. Streaming. Near real-time. These are all terms used to identify the business’s need for quick access to data. It’s a common request, even if the data must flow from on-premises to the cloud. Oracle GoldenGate is the data replication solution built for fast data. In this session, we’ll look at how GoldenGate can be configured to extract transactions from the Oracle database and load them into a cloud object store, such as Amazon S3. There are many different use cases for this type of continuous load of data into the cloud. We’ll explore these solutions and the various tools that can be used to access and analyze the data from the cloud object store, leaving attendees with ideas for implementing a full source-to-cloud data replication solution.
Presented at ITOUG Tech Days 2019
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
The Data Lake Engine Data Microservices in Spark using Apache Arrow FlightDatabricks
Machine learning pipelines are a hot topic at the moment. Moving data through the pipeline in an efficient and predictable way is one of the most important aspects of running machine learning models in production.
Lambda Architecture in the Cloud with Azure Databricks with Andrei VaranovichDatabricks
The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising.
In this talk we demonstrate the blueprint for such an implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component. We go back to some core principles of functional programming and link them to the capabilities of Apache Spark for various end-to-end big data analytics scenarios.
We also illustrate the “Lambda architecture in use” and the associated tread-offs using the real customer scenario – Rijksmuseum in Amsterdam – a terabyte-scale Azure-based data platform handles data from 2.500.000 visitors per year.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Introduction to failover clustering with sql serverEduardo Castro
In this presentation we review the basic requirements to install a SQL Server Failover Cluster.
Regards,
Eduardo Castro Martinez
http://ecastrom.blogspot.com
http://comunidadwindows.org
Modeling Data and Queries for Wide Column NoSQLScyllaDB
Discover how to model data for wide column databases such as ScyllaDB and Apache Cassandra. Contrast the differerence from traditional RDBMS data modeling, going from a normalized “schema first” design to a denormalized “query first” design. Plus how to use advanced features like secondary indexes and materialized views to use the same base table to get the answers you need.
Siloed data is difficult to access and causes data consumers to only have partial views of the problem at hand. By limiting access to large volumes of disparate data, analysts and business users alike don’t have the ability to included important data in their reports and models leading to suboptimal analytic outputs. Even when this data is available to countless users, traditional systems limit them to querying small volumes of data in order to return the results in a timely matter.
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
The Transformation of your Data in modern IT (Presented by DellEMC)Cloudera, Inc.
Organizations have a wealth of data contained within the existing infrastructures. At DellEMC we’re helping customers remove the barriers of legacy datastores and transforming the customer experience in the modern datacentre. Learn how to unshackle the valuable data inside your existing data warehouse, leverage new techniques, applications and technology to enhance the financial impact of all your data sources
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization Denodo
Watch here: https://bit.ly/2NGQD7R
In an era increasingly dominated by advancements in cloud computing, AI and advanced analytics it may come as a shock that many organizations still rely on data architectures built before the turn of the century. But that scenario is rapidly changing with the increasing adoption of real-time data virtualization - a paradigm shift in the approach that organizations take towards accessing, integrating, and provisioning data required to meet business goals.
As data analytics and data-driven intelligence takes centre stage in today’s digital economy, logical data integration across the widest variety of data sources, with proper security and governance structure in place has become mission-critical.
Attend this session to learn:
- Learn how you can meet cloud and data science challenges with data virtualization.
- Why data virtualization is increasingly finding enterprise-wide adoption
- Discover how customers are reducing costs and improving ROI with data virtualization
Turning Petabytes of Data into Profit with Hadoop for the World’s Biggest Ret...Cloudera, Inc.
PRGX is the world's leading provider of accounts payable audit services and works with leading global retailers. As new forms of data started to flow into their organizations, standard RDBMS systems were not allowing them to scale. Now, by using Talend with Cloudera Enterprise, they are able to acheive a 9-10x performance benefit in processing data, reduce errors, and now provide more innovative products and services to end customers.
Watch this webinar to learn how PRGX worked with Cloudera and Talend to create a high-performance computing platform for data analytics and discovery that rapidly allows them to process, model, and serve massive amount of structured and unstructured data.
Google take on heterogeneous data base replication Svetlin Stanchev
Datastream from Google is a serverless change data capture and replication service. This allows organizations to replicate data across multiple databases, storage systems and is especially useful for replicating OLTP data in MySQL into an OLAP database such as BigQuery. This talk walks through setting up connection profiles, streams and touch on some useful debugging if things don't go as planned
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
times ten in-memory database for extreme performanceOracle Korea
어디서나 업무가 가능한 모바일 시대가 되면서 비약적으로 데이터 사이즈가 커지고 이를 처리하기 위해서는 고성능의 빠른 Database가 필요하게 되었습니다. 이러한 요구사항을 반영하여 기존에 우리가 잘 사용하고있던 Database 들도 In-Memory 기술을 속속 도입하고 있습니다. In-Memory 기술은 이전부터 있었지만 하드웨어의 한계와 소프트웨어의 확정성의 부족으로 많이 사용되지 않았던 기술입니다.
Oracle TimesTen 18.1은 기존 In-Memory Database가 가지는 한계를 극복하고, 빠른 처리 속도와 확장(Scaleout)가능한 분산 아키텍처를 지원하는 In-Memory 관계형 Database 입니다.
본 세션에서는 Oracle TimesTen의 분산 아키텍처와 주요 Feature를 소개하고 TimesTen 최신버전인 18.1의 데모를 진행할 예정입니다. 또한 현재 TimesTen을 이용하여 국내 통신사의 서비스를 개발하고 있는 이루온의 실제 적용 사례 및 성능 테스트 결과를 공유하는 시간이 될 것입니다.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Syncsort, Tableau, & Cloudera present: Break the Barriers to Big Data InsightCloudera, Inc.
Rethink data management and learn how to break down barriers to Big Data insight with Cloudera's enterprise data hub (EDH), Syncsort offload solutions, and Tableau Software visualization and analytics.
Syncsort, Tableau, & Cloudera present: Break the Barriers to Big Data InsightSteven Totman
Demand for quicker access to multiple integrated sources of data continues to rise. Immediate access to data stored in a variety of systems - such as mainframes, data warehouses, and data marts - to mine visually for business intelligence is the competitive differentiation enterprises need to win in today’s economy.
Stop playing the waiting game and learn about a new end-to-end solution for combining, analyzing, and visualizing data from practically any source in your enterprise environment.
Leading organizations are already taking advantage of this architectural innovation to gain modern insights while reducing costs and propelling their businesses ahead of the competition.
Are you tired of waiting? Don't let your architecture hold you back. Access this webinar and hear from a team of industry experts on how you can Break the Barriers to Big Data Insight.
Many companies today move mountains of data using ETL (extract, transform, load) technology. But data volumes are growing too large to move, customers are now expecting real-time data, and ETL costs now account for 10-15% of computing capacity. In this slide presentation, you can see how data virtualization enables data structures that were designed independently to be leveraged together, in real time, and without data movement, reducing complexity, lowering IT costs, and minimizing risk.
Breakout: Operational Analytics with HadoopCloudera, Inc.
Operationalizing models and responding to large volumes of data, fast, requires bolt on systems that can struggle with processing (transforming the data), consistency (always responding to data), and scalability (processing and responding to large volumes of data). If the data volume become too large, these traditional systems fail to deliver their responses resulting in significant losses to organizations. Join this breakout to learn how to overcome the roadblocks.
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
Syncsort, Tableau, & Cloudera present: Break the Barriers to Big Data InsightPrecisely
Demand for quicker access to multiple integrated sources of data continues to rise. Immediate access to data stored in a variety of systems - such as mainframes, data warehouses, and data marts - to mine visually for business intelligence is the competitive differentiation enterprises need to win in today’s economy.
Stop playing the waiting game and learn about a new end-to-end solution for combining, analyzing, and visualizing data from practically any source in your enterprise environment.
Leading organizations are already taking advantage of this architectural innovation to gain modern insights while reducing costs and propelling their businesses ahead of the competition.
Are you tired of waiting? Don't let your architecture hold you back. Access this webinar and hear from a team of industry experts on how you can Break the Barriers to Big Data Insight.
Similar to Breakout: Hadoop and the Operational Data Store (20)
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
This annual program recognizes organizations who are moving swiftly towards the future and building innovative solutions by making what was impossible yesterday, possible today.
The winning organizations' implementations demonstrate outstanding achievements in fulfilling their mission, technical advancement, and overall impact.
The 2021 Data Impact Awards recognize organizations' achievements with the Cloudera Data Platform in seven categories:
Data Lifecycle Connection
Data for Enterprise AI
Cloud Innovation
Security & Governance Leadership
People First
Data for Good
Industry Transformation
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.
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.
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.
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.
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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.
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
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.
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.
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
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
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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
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Data storage costs: http://thecaucus.blogs.nytimes.com/2012/08/14/advances-in-data-storage-have-implications-for-government-surveillance/IoT: http://www.forbes.com/sites/gilpress/2014/08/22/internet-of-things-by-the-numbers-market-estimates-and-forecasts/
Resource Intensive ELT: http://www.syncsort.com/getattachment/45696aa9-1e40-43cb-8905-b9fc7e2519f7/Syncsort-Data-Warehouse-Offload-Solution.aspx
An Operational Data Store provides a staging environment in order to ingest, store, and process data in preparation for operational and analytical use.
Depending on whether or not this data is structured or unstructured, different systems can be used to optimize data pipelines.
The only challenge is that as your organization continues to ask for larger volumes of diverse data, traditional systems face issues.
These challenges specifically arise around data storage and processing.
The first challenge is limited data access. Collecting and ingesting a wide variety of diverse data is not a simple task and usually results in additional systems, or capacity being added to the architecture. As the business continues to ask for more data this continues to put strains on IT. In order to avoid these challenges only the most valuable data is brought in, limiting the businesses access to data that could be extremely valuable.
The second challenges that we see organizations try to hurdle is around processing data volumes. These organizations have already collected and operationalized large volumes of data and need to process this data efficiently in order to meet SLAs. If data doesn’t reach the employees in a timely manner then they continue on without the most recent information.
The third and final set of challenges is around archiving data. When systems reach capacity as larger volumes of diverse data is leveraged within an organization, this causes IT professionals to archive or delete data that has been deemed “invaluable”. When data is moved offline to an archive, this significantly reduces the return on the data and can hurt the business. This data can be extremely important as analyst attempt to find patterns in historic data but can’t access this information because it’s offline.
However, as the external and internal data environment has changed over the years so has the data management space.
These challenges specifically arise around data storage and processing.
The first challenge is limited data access. Collecting and ingesting a wide variety of diverse data is not a simple task and usually results in additional systems, or capacity being added to the architecture. As the business continues to ask for more data this continues to put strains on IT. In order to avoid these challenges only the most valuable data is brought in, limiting the businesses access to data that could be extremely valuable.
The second challenges that we see organizations try to hurdle is around processing data volumes. These organizations have already collected and operationalized large volumes of data and need to process this data efficiently in order to meet SLAs. If data doesn’t reach the employees in a timely manner then they continue on without the most recent information.
The third and final set of challenges is around archiving data. When systems reach capacity as larger volumes of diverse data is leveraged within an organization, this causes IT professionals to archive or delete data that has been deemed “invaluable”. When data is moved offline to an archive, this significantly reduces the return on the data and can hurt the business. This data can be extremely important as analyst attempt to find patterns in historic data but can’t access this information because it’s offline.
However, as the external and internal data environment has changed over the years so has the data management space.
These challenges specifically arise around data storage and processing.
The first challenge is limited data access. Collecting and ingesting a wide variety of diverse data is not a simple task and usually results in additional systems, or capacity being added to the architecture. As the business continues to ask for more data this continues to put strains on IT. In order to avoid these challenges only the most valuable data is brought in, limiting the businesses access to data that could be extremely valuable.
The second challenges that we see organizations try to hurdle is around processing data volumes. These organizations have already collected and operationalized large volumes of data and need to process this data efficiently in order to meet SLAs. If data doesn’t reach the employees in a timely manner then they continue on without the most recent information.
The third and final set of challenges is around archiving data. When systems reach capacity as larger volumes of diverse data is leveraged within an organization, this causes IT professionals to archive or delete data that has been deemed “invaluable”. When data is moved offline to an archive, this significantly reduces the return on the data and can hurt the business. This data can be extremely important as analyst attempt to find patterns in historic data but can’t access this information because it’s offline.
However, as the external and internal data environment has changed over the years so has the data management space.
We have been working closely with leading organizations to create a platform that allows them to complement their current architecture in order to avoid these common challenges. This in turn prepares for future growth of data within their organizations.
Ingest More Data-
Cloudera allows you to collect and ingest any data type or volume of data, in full fidelity, in order to allow for complete data access to your current systems and end users. This has allowed organizations to collect and access more diverse data, opening up the possibilities of what data can do for the business, without compromising system performance or existing resource constraints.
Efficiently Process & Store Data Volumes-
By offloading heavy processing workloads to Cloudera, organizations are able to use parallel processing in order to significantly reduce processing time on large volumes of data. With the scalable nature of Cloudera, you also ensure that no matter how much data is stored the platform continues to perform at peak performance.
Automated Secure Archive-
Leveraging Cloudera as an ODS and using it as a centralized staging environment for new data allows you to automatically create a secure archive. Because of the platform’s scalable nature, there is never a reason to archive your data. Historic data can remain on the platform for analysts allowing them complete access without derogating system performance. While smaller volumes of already defined active data can run directly into the right systems, with outdated data being offloaded to Cloudera.
Leading data organizations have already seen these benefits.
We have been working closely with leading organizations to create a platform that allows them to complement their current architecture in order to avoid these common challenges. This in turn prepares for future growth of data within their organizations.
Ingest More Data-
Cloudera allows you to collect and ingest any data type or volume of data, in full fidelity, in order to allow for complete data access to your current systems and end users. This has allowed organizations to collect and access more diverse data, opening up the possibilities of what data can do for the business, without compromising system performance or existing resource constraints.
Efficiently Process & Store Data Volumes-
By offloading heavy processing workloads to Cloudera, organizations are able to use parallel processing in order to significantly reduce processing time on large volumes of data. With the scalable nature of Cloudera, you also ensure that no matter how much data is stored the platform continues to perform at peak performance.
Automated Secure Archive-
Leveraging Cloudera as an ODS and using it as a centralized staging environment for new data allows you to automatically create a secure archive. Because of the platform’s scalable nature, there is never a reason to archive your data. Historic data can remain on the platform for analysts allowing them complete access without derogating system performance. While smaller volumes of already defined active data can run directly into the right systems, with outdated data being offloaded to Cloudera.
Leading data organizations have already seen these benefits.
We have been working closely with leading organizations to create a platform that allows them to complement their current architecture in order to avoid these common challenges. This in turn prepares for future growth of data within their organizations.
Ingest More Data-
Cloudera allows you to collect and ingest any data type or volume of data, in full fidelity, in order to allow for complete data access to your current systems and end users. This has allowed organizations to collect and access more diverse data, opening up the possibilities of what data can do for the business, without compromising system performance or existing resource constraints.
Efficiently Process & Store Data Volumes-
By offloading heavy processing workloads to Cloudera, organizations are able to use parallel processing in order to significantly reduce processing time on large volumes of data. With the scalable nature of Cloudera, you also ensure that no matter how much data is stored the platform continues to perform at peak performance.
Automated Secure Archive-
Leveraging Cloudera as an ODS and using it as a centralized staging environment for new data allows you to automatically create a secure archive. Because of the platform’s scalable nature, there is never a reason to archive your data. Historic data can remain on the platform for analysts allowing them complete access without derogating system performance. While smaller volumes of already defined active data can run directly into the right systems, with outdated data being offloaded to Cloudera.
Leading data organizations have already seen these benefits.