Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
New! Real-Time Data Replication to SnowflakePrecisely
Your business is adopting the Snowflake cloud data platform to rapidly deliver data insights and lower the costs of your data warehouse. But you have a problem – what happens when data changes on your mainframe and IBM i systems? How do you make sure Snowflake is always up-to-date and in sync with these systems of record?
If you can’t integrate changes occurring on your mainframe and IBM i systems to Snowflake, your business will miss the critical data it needs to drive real-time insights and decision making.
Join us to learn how the latest enhancements to Precisely Connect help your business meet its data-driven goals by sharing changes made on legacy, mainframe, and IBM systems to Snowflake in real time.
During this webinar, you will learn more about:
- How to easily support data replication from mainframe and IBM i to Snowflake
- Connect’s enhanced data replication capabilities for cloud data platforms
- How customers are using Connect to support their cloud data platform strategies
Data & Analytics - Session 2 - Introducing Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast and powerful, fully managed, petabyte-scale data warehouse service in the cloud. This presentation will give an introduction to the service and its pricing before diving into how it delivers fast query performance on data sets ranging from hundreds of gigabytes to a petabyte or more.
Steffen Krause, Technical Evangelist, AWS
Padraic Mulligan, Architect and Lead Developer and Mike McCarthy, CTO, Skillspage
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...Databricks
Columbia is a data-driven enterprise, integrating data from all line-of-business-systems to manage its wholesale and retail businesses. This includes integrating real-time and batch data to better manage purchase orders and generate accurate consumer demand forecasts.
Abstract: Data preparation and modelling are the activities that take most of the time in a typical data scientist workday. In this session we’ll see how AWS services for Analytics and data management can be effectively used and integrated in AI/ML pipelines. We’ll focus on AWS Glue, AWS Glue DataBrew and AWS Data Wrangler with a bit of theory and hands-on demos.
Bio:
Francesco Marelli is a senior solutions architect at Amazon Web Services. He has lived and worked in UK, italy, Switzerland and other countries in EMEA. He is specialized in the design and implementation of Analytics, Data Management and Big Data systems. Francesco also has a strong experience in systems integration and design and implementation of applications.
Topics: machine learning pipelines, AWS, cloud.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
IBM THINK 2019 - A Sharing Economy for Analytics: SQL Query in IBM CloudTorsten Steinbach
Cloud is a sharing economy that reduces your spending. But does this also apply to data and analytics? Doesn't this require you to provision dedicated data warehouse systems to run analytics SQL queries on terabytes of data? With IBM Cloud, the answer is no. By using serverless analytics via IBM Cloud SQL Query, you can analyze your data directly where it sits, be it in IBM Cloud Object Storage or in your NoSQL databases. Due to the serverless nature of SQL Query, you only pay for your queries depending on the data volume that they process. There are no standing costs. You do not need to provision and wait for a data warehouse. But you can still run SQLs on terabytes of data.
Streaming Real-time Data to Azure Data Lake Storage Gen 2Carole Gunst
Check out this presentation to learn the basics of using Attunity Replicate to stream real-time data to Azure Data Lake Storage Gen2 for analytics projects.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Vivint Smart Home's journey with Snowflake and migrating from SQL Server. We describe how we have setup snowflake from a people, process, and technology perspective.
Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Im...Databricks
Join this session to hear why Smartsheet decided to transition from their entirely SQL-based system to Snowflake and Databricks, and learn how that transition has made an immediate impact on their team, company and customer experience through enabling faster, informed data decisions.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Making the Most of Data in Multiple Data Sources (with Virtual Data Lakes)DataWorks Summit
Most organizations today implement different data stores to support business operations. As a result, data ends up stored across a multitude of often heterogenous systems, like RDBMS, NoSQL, data warehouses, data marts, Hadoop, etc., with limited interaction and/or interoperability between them. The end result is often a vast eco-system of data stores with different "temperature" data, some level of duplication and, no effective way of bringing it all together for business analytics. With such disparate data, how can an organization exploit the wealth of information? This opens up the need for proven techniques to quickly and easily deliver the data to the people who need it. In this session, you'll see how to modernize your enterprise by making data accessible with enterprise capabilities like querying using SQL, granular security for data access, and maintaining high query performance and high concurrency.
Big Data LDN 2018: A TALE OF TWO BI STANDARDS: DATA WAREHOUSES AND DATA LAKESMatt Stubbs
Date: 13th November 2018
Location: Self-Service Analytics Theatre
Time: 14:30 - 15:00
Speaker: Zaf Khan
Organisation: Arcadia Data
About: The use of data lakes continue to grow, and a recent survey by Eckerson Group shows that organizations are getting real value from their deployments. However, there’s still a lot of room for improvement when it comes to giving business users access to the wealth of potential insights in the data lake.
While the data management aspect has been fairly well understood over the years, the success of business intelligence (BI) and analytics on data lakes lags behind. In fact, organizations often struggle with data lakes because they are only accessible by highly-skilled data scientists and not by business users. But BI tools have been able to access data warehouses for years, so what gives?
In this talk, we’ll discuss:
• Why traditional BI tools are architected well for data warehouses, but not data lakes.
• Why every organization should have two BI standards: one for data warehouses and one for data lakes.
• Innovative capabilities provided by BI for data lakes
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
New! Real-Time Data Replication to SnowflakePrecisely
Your business is adopting the Snowflake cloud data platform to rapidly deliver data insights and lower the costs of your data warehouse. But you have a problem – what happens when data changes on your mainframe and IBM i systems? How do you make sure Snowflake is always up-to-date and in sync with these systems of record?
If you can’t integrate changes occurring on your mainframe and IBM i systems to Snowflake, your business will miss the critical data it needs to drive real-time insights and decision making.
Join us to learn how the latest enhancements to Precisely Connect help your business meet its data-driven goals by sharing changes made on legacy, mainframe, and IBM systems to Snowflake in real time.
During this webinar, you will learn more about:
- How to easily support data replication from mainframe and IBM i to Snowflake
- Connect’s enhanced data replication capabilities for cloud data platforms
- How customers are using Connect to support their cloud data platform strategies
Data & Analytics - Session 2 - Introducing Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast and powerful, fully managed, petabyte-scale data warehouse service in the cloud. This presentation will give an introduction to the service and its pricing before diving into how it delivers fast query performance on data sets ranging from hundreds of gigabytes to a petabyte or more.
Steffen Krause, Technical Evangelist, AWS
Padraic Mulligan, Architect and Lead Developer and Mike McCarthy, CTO, Skillspage
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...Databricks
Columbia is a data-driven enterprise, integrating data from all line-of-business-systems to manage its wholesale and retail businesses. This includes integrating real-time and batch data to better manage purchase orders and generate accurate consumer demand forecasts.
Abstract: Data preparation and modelling are the activities that take most of the time in a typical data scientist workday. In this session we’ll see how AWS services for Analytics and data management can be effectively used and integrated in AI/ML pipelines. We’ll focus on AWS Glue, AWS Glue DataBrew and AWS Data Wrangler with a bit of theory and hands-on demos.
Bio:
Francesco Marelli is a senior solutions architect at Amazon Web Services. He has lived and worked in UK, italy, Switzerland and other countries in EMEA. He is specialized in the design and implementation of Analytics, Data Management and Big Data systems. Francesco also has a strong experience in systems integration and design and implementation of applications.
Topics: machine learning pipelines, AWS, cloud.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
IBM THINK 2019 - A Sharing Economy for Analytics: SQL Query in IBM CloudTorsten Steinbach
Cloud is a sharing economy that reduces your spending. But does this also apply to data and analytics? Doesn't this require you to provision dedicated data warehouse systems to run analytics SQL queries on terabytes of data? With IBM Cloud, the answer is no. By using serverless analytics via IBM Cloud SQL Query, you can analyze your data directly where it sits, be it in IBM Cloud Object Storage or in your NoSQL databases. Due to the serverless nature of SQL Query, you only pay for your queries depending on the data volume that they process. There are no standing costs. You do not need to provision and wait for a data warehouse. But you can still run SQLs on terabytes of data.
Streaming Real-time Data to Azure Data Lake Storage Gen 2Carole Gunst
Check out this presentation to learn the basics of using Attunity Replicate to stream real-time data to Azure Data Lake Storage Gen2 for analytics projects.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Vivint Smart Home's journey with Snowflake and migrating from SQL Server. We describe how we have setup snowflake from a people, process, and technology perspective.
Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Im...Databricks
Join this session to hear why Smartsheet decided to transition from their entirely SQL-based system to Snowflake and Databricks, and learn how that transition has made an immediate impact on their team, company and customer experience through enabling faster, informed data decisions.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Making the Most of Data in Multiple Data Sources (with Virtual Data Lakes)DataWorks Summit
Most organizations today implement different data stores to support business operations. As a result, data ends up stored across a multitude of often heterogenous systems, like RDBMS, NoSQL, data warehouses, data marts, Hadoop, etc., with limited interaction and/or interoperability between them. The end result is often a vast eco-system of data stores with different "temperature" data, some level of duplication and, no effective way of bringing it all together for business analytics. With such disparate data, how can an organization exploit the wealth of information? This opens up the need for proven techniques to quickly and easily deliver the data to the people who need it. In this session, you'll see how to modernize your enterprise by making data accessible with enterprise capabilities like querying using SQL, granular security for data access, and maintaining high query performance and high concurrency.
Big Data LDN 2018: A TALE OF TWO BI STANDARDS: DATA WAREHOUSES AND DATA LAKESMatt Stubbs
Date: 13th November 2018
Location: Self-Service Analytics Theatre
Time: 14:30 - 15:00
Speaker: Zaf Khan
Organisation: Arcadia Data
About: The use of data lakes continue to grow, and a recent survey by Eckerson Group shows that organizations are getting real value from their deployments. However, there’s still a lot of room for improvement when it comes to giving business users access to the wealth of potential insights in the data lake.
While the data management aspect has been fairly well understood over the years, the success of business intelligence (BI) and analytics on data lakes lags behind. In fact, organizations often struggle with data lakes because they are only accessible by highly-skilled data scientists and not by business users. But BI tools have been able to access data warehouses for years, so what gives?
In this talk, we’ll discuss:
• Why traditional BI tools are architected well for data warehouses, but not data lakes.
• Why every organization should have two BI standards: one for data warehouses and one for data lakes.
• Innovative capabilities provided by BI for data lakes
Prezentace z webináře dne 10.3.2022
Prezentovali:
Jaroslav Malina - Senior Channel Sales Manager, Oracle
Josef Krejčí - Technology Sales Consultant, Oracle
Josef Šlahůnek - Cloud Systems sales Consultant, Oracle
Production-Ready Environments for Kubernetes (CON307-S) - AWS re:Invent 2018Amazon Web Services
Kubernetes is taking off and being rapidly adopted both on-premises and in the AWS Cloud. Today, enterprises are struggling to build, deploy, and manage production-ready environments at scale. The Cisco Hybrid Solution for Kubernetes on AWS makes it easy for customers to run production-grade Kubernetes on-premises. This is achieved by configuring on-premises Kubernetes environments to be consistent with Amazon Elastic Container Service for Kubernetes (Amazon EKS) and by combining Cisco's networking, security, management, and monitoring software with the world-class cloud services of AWS. This enables customers to focus on building and using applications instead of being constrained by where they run.
SendGrid Improves Email Delivery with Hybrid Data WarehousingAmazon Web Services
When you received your Uber ‘Tuesday Evening Ride Receipt’ or Spotify’s ‘This Week’s New Music’ email, did you think about how they got there?
SendGrid’s reliable email platform delivers each month over 20 Billion transactional and marketing emails on behalf of many of your favorite brands, including Uber, Airbnb, Spotify, Foursquare and NextDoor.
SendGrid was looking to evolve its data warehouse architecture in order to improve decision making and optimize customer experience. They needed a scalable and reliable architecture that would allow them to move nimbly and efficiently with a relatively small IT organization, while supporting the needs of both business and technical users at SendGrid.
SendGrid’s Director of Enterprise Data Operations will be joining architects from Amazon Web Services (AWS) and Informatica to discuss SendGrid’s journey to a hybrid cloud architecture and how a hybrid data warehousing solution is optimized to support SendGrid’s analytics initiative. Speakers will also review common technologies and use cases being deployed in hybrid cloud today, common data management challenges in hybrid cloud and best practices for addressing these challenges.
Join us to learn:
• How to evolve to a hybrid data warehouse with Amazon Redshift for scalability, agility and cost efficiency with minimal IT resources
• Hybrid cloud data management use cases
• Best practices for addressing hybrid cloud data management challenges
ICP for Data- Enterprise platform for AI, ML and Data ScienceKaran Sachdeva
IBM Cloud Private for Data, an ultimate platform for all AI, ML and Data Science workloads. Integrated analytics platform based on Containers and micro services. Works with Kubernetes and dockers, even with Redhat openshift. Delivers the variety of business use cases in all industries- FS, Telco, Retail, Manufacturing etc
Data Con LA 2018 - A tale of two BI standards: Data warehouses and data lakes...Data Con LA
A tale of two BI standards: Data warehouses and data lakes by Shant Hovsepian, Co-Founder and CTO, Arcadia Data
Data lakes as part of the logical data warehouse (LDW) have entered the trough of disillusionment. Some failures are due to lack of value from businesses focusing on the big data challenges and not the big analytics opportunity. After all, data is just data until you analyze it. While the data management aspect has been fairly well understood over the years, the success of business intelligence (BI) and analytics on data lakes lags behind. In fact, data lakes often fail because they are only accessible by highly skilled data scientists and not by business users. But BI tools have been able to access data warehouses for years, so what gives? Shant Hovsepian explains why existing BI tools are architected well for data warehouses but not data lakes, the pros and cons of each architecture, and why every organization should have two BI standards: one for data warehouses and one for data lakes.
Liberate Legacy Data Sources with Precisely and DatabricksPrecisely
Mainframe and IBM i data continues to be prevalent in several industries including financial services, insurance, and retail where critical customer information lives on legacy systems. In fact, in 2019 alone, studies show that there was a 55% increase in transaction volumes on the mainframe across all industries. To thrive in highly competitive markets, you must quickly break down legacy data silos to swiftly gain a full picture of data for insights for strategic action.
Traditional storage solutions that are mainframe proprietary struggle to scale for high data volumes and real-time analytics use cases. This results in increased costs, diminished performance, and missed SLAs. To solve this, Precisely and Databricks provide a modern approach for organizations to optimize volumes of data by leveraging the massive scalability of the cloud to power high-performance analytics, AI, and machine learning, regardless of where data lives.
In this webinar, we discuss:
- Quickly ingesting data from on-premises sources – such as mainframe and IBM i – to the cloud with the Databricks Unified Data Analytics Platform and Delta Lake
- Modernizing ETL processes and reduce development costs with visual data pipelines that uses the elastic scalability of Databricks
- Empowering business users with the most up to date data by populating Delta Lake with realtime data changes from legacy systems
View this webinar on-demand to see a live demo of the joint solution and how it can modernize your legacy infrastructure
IBM THINK 2019 - What? I Don't Need a Database to Do All That with SQL?Torsten Steinbach
You don't necessarily have to set up a relational database, tables and load data in order to use a surprisingly rich set of SQL capabilities on your data in the cloud. IBM SQL Query lets you analyze terabytes of distributed data of heterogeneous formats with a complete ANSI SQL dialect in a completely serverless usage model, elegantly ETL data between formats and partitioning layouts as needed, and run complex time series transformations, analysis and correlations with advanced built-in timeseries SQL algorithms that are differentiating in the entire industry. It also support a complete PostGIS compliant geospatial SQL function set. Come explore the stunningly advanced world of SQL without a database in IBM Cloud.
Transforming Enterprise IT - Virtual Transformation Day Feb 2019Amazon Web Services
Speaker: Wesley Wilks, Dan Gallivan
As more and more enterprises start down the path of their digital transformation, the pressure on their IT organizations to support innovation across the business couldn’t be higher. In this session, we will outline a number of cutting-edge technologies as well as an operating model that will allow IT to position itself as a business enabler and not a blocker. We will be sharing some mechanisms that will enable the IT organization to meet the pace of innovation that is being set by the business while giving them the flexibility to leverage existing assets.
AWS Transformation Day is designed for enterprise organizations looking to make the move to the cloud in order to become more responsive, agile and innovative, while still staying secure and compliant. Join us for this virtual event and we'll share our experiences of helping enterprise customers accelerate the pace of migration and adoption of strategic services.
We recommend this event for IT and business leaders who are looking to create sustainable benefits and a competitive advantage by using the AWS Cloud.
dashDB Enterprise MPP is a new fully managed cloud data warehouse service with massive scale and performance. Powered by IBM's network cluster architecture, dashDB MPP is an easy to use, self service solution for building: standalone data warehouses; data science data marts; hybrid warehousing; development and QA environments; and analytics for NoSQL. It is available through IBM Bluemix along with IBM's other Cloud Data Services, including Cloudant and SQL DB.
Learn about data lifecycle best practices in the AWS Cloud. Discover how to optimise performance and lower the costs of data ingestion, staging, storage, cleansing, analytics, visualisation, and archiving.
Similar to IBM THINK 2020 - Cloud Data Lake with IBM Cloud Data Services (20)
IBM THINK 2019 - Cloud-Native Clickstream Analysis in IBM CloudTorsten Steinbach
Agile user and workload insights are one of the key elements of a cloud-native solution. When done well, this represents a real competitive advantage. In this session, we show you how to run cloud-native clickstream analysis with IBM Cloud. By combining serverless mechanisms like object storage for affordable and scalable persistency with SQL Query for serverless analysis of your clickstream data, you can establish a very cost-effective clickstream analysis pipeline easily and quickly.
IBM THINK 2019 - Self-Service Cloud Data Management with SQL Torsten Steinbach
SQL is a powerful language to express data transformations. But did you know that you can also use IBM Cloud SQL to convert data between various data formats and layouts on disks? In this session, you will see the full power of using SQL Query to move and transform your cloud data in an entirely self-service fashion. You can specify any data format, layout or partitioning with a simple SQL statement. See how you can move and transform terabytes of data in the cloud in a very scalable fashion and still being charged only for the individual SQL movement and transformation jobs without having standing costs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
6. IBM Cloud Data Services
empowering Cloud Data Lakes
Enterprise
GradeOpen Secure
Object StorageElasticsearchMongoDB RedisPostgreSQL
RabbitMQ
etcdCloudant
Relational Non-Relational Persistent Storage
Data
Stores
IBMCloudDataServices
Data
Movement and
Action Event Streams SQL Query
DataServicesSolutions
CloudDataLake,etc.
Data lakes are not a completely new thing: Common solution found in enterprises implemented with a traditional form factor of the past: Dedicated Hadoop Clusters. Heavy modernization need and opportunity.
Now Data Lakes are evolving to Cloud Native
Client Users
Data architects
Responsible for an organizations data architecture
Business and Data Analysts
Generate and analyze reports on specific data in the organization to provide business insight
Data scientists and application developers
Perform statistical analysis on big data to identify trends
Solve business problems
Optimize performance
I think the arrow graphic on top is confusing, why is it on this slide. IS this slides showing the process flow of data lake or why data lake?
I think the arrow graphic on top is confusing, why is it on this slide. IS this slides showing the process flow of data lake or why data lake?
Advantages over others:
FIPS 140-2 Level 4. Others are in Level 2 or 3
99.9% SLA for non HA workloads. We offer this. AWS & Google don’t
Integrated IaaS & PaaS SLAs. We are on par with others
Audit - Traceability to Serial number. We can do it. AWS, Azure cannot. Google does not have a Bare metal offering. Oracle can.
Kubernetes on Bare metal (for heavy AI, Analytics). Only we can do it. AWS, Azure, Google cannot.
Automated Day 2 management of Container platforms with Red Hat OpenShift. AWS, Google cannot do this
Guarantee that your data is not used to fine tune vendor’s AI models. AWS, Azure don’t give this assurance