Modern data solution like Lyftron eliminate the time spent by engineers building Snowflake data pipelines manually– and make data instantly accessible to analysts by providing real-time access to all your data with simple ANSI SQL.
Load data from Quickbook to Snowflake in minutessyed_javed
Modern data solution like Lyftron enables data governance with data catalog, data model, data definition, data lineage, tagging and enterprise data dictionary search.
Empowering Real Time Patient Care Through Spark StreamingDatabricks
Takeda’s Plasma Derived Therapies (PDT) business unit has recently embarked on a project to use Spark Streaming on Databricks to empower how they deliver value to their Plasma Donation centers. As patients come in and interface without clinics, we store and track all of the patient interactions in real time and deliver outputs and results based on said interactions. The current problem with our existing architecture is that it is very expensive to maintain and has an unsustainable number of failure points. Spark Streaming is essential for allowing this use case because it allows for a more robust ETL pipeline. With Spark Streaming, we are able to replace our existing ETL processes (that are based on Lamdbas, step functions, triggered jobs, etc) into a purely stream driven architecture.
Data is brought into our s3 raw layer as a large set of CSV files through AWS DMS and Informatica IICS as these services bring data from on-prem systems into our cloud layer. We have a stream currently running which takes these raw files up and merges them into Delta tables established in the bronze/stage layer. We are using AWS Glue as the metadata provider for all of these operations. From the stage layer, we have another set of streams using the stage Delta tables as their source, which transform and conduct stream to stream lookups before writing the enriched records into RDS (silver/prod layer). Once the data has been merged into RDS we have a DMS task which lifts the data back into S3 as CSV files. We have a small intermediary stream which merge these CSV files into corresponding delta tables, from which we have our gold/analytic streams. The on-prem systems are able to speak to the silver layer and allow for the near real-time latency that our patient care centers require.
Transforming data into actionable insightsElasticsearch
Learn about the strategic feature areas of the Elastic Stack—Elasticsearch, a data engine like no other, and Kibana, the window into the Elastic Stack.
The session will cover:
Bringing data into the Elastic Stack
Storing data
Analyzing data
Acting on data
Misusing MLflow To Help Deduplicate Data At ScaleDatabricks
At Intuit, we have a lot of data – and a lot of duplicate data collected over decades. So we built a rule-based, self-serve tool to identify and merge duplicate records. It takes experimentation and iteration to get deduplication just right for 100s of millions of records, and spreadsheet-based tracking just wasn’t enough. We now use MLflow to automatically capture execution notes, rule settings, weights, key validation metrics, etc., all without requiring end-user action. In this talk, we’ll talk about our use case and why MLflow is useful outside its traditional ML Ops use cases.
Load data from Quickbook to Snowflake in minutessyed_javed
Modern data solution like Lyftron enables data governance with data catalog, data model, data definition, data lineage, tagging and enterprise data dictionary search.
Empowering Real Time Patient Care Through Spark StreamingDatabricks
Takeda’s Plasma Derived Therapies (PDT) business unit has recently embarked on a project to use Spark Streaming on Databricks to empower how they deliver value to their Plasma Donation centers. As patients come in and interface without clinics, we store and track all of the patient interactions in real time and deliver outputs and results based on said interactions. The current problem with our existing architecture is that it is very expensive to maintain and has an unsustainable number of failure points. Spark Streaming is essential for allowing this use case because it allows for a more robust ETL pipeline. With Spark Streaming, we are able to replace our existing ETL processes (that are based on Lamdbas, step functions, triggered jobs, etc) into a purely stream driven architecture.
Data is brought into our s3 raw layer as a large set of CSV files through AWS DMS and Informatica IICS as these services bring data from on-prem systems into our cloud layer. We have a stream currently running which takes these raw files up and merges them into Delta tables established in the bronze/stage layer. We are using AWS Glue as the metadata provider for all of these operations. From the stage layer, we have another set of streams using the stage Delta tables as their source, which transform and conduct stream to stream lookups before writing the enriched records into RDS (silver/prod layer). Once the data has been merged into RDS we have a DMS task which lifts the data back into S3 as CSV files. We have a small intermediary stream which merge these CSV files into corresponding delta tables, from which we have our gold/analytic streams. The on-prem systems are able to speak to the silver layer and allow for the near real-time latency that our patient care centers require.
Transforming data into actionable insightsElasticsearch
Learn about the strategic feature areas of the Elastic Stack—Elasticsearch, a data engine like no other, and Kibana, the window into the Elastic Stack.
The session will cover:
Bringing data into the Elastic Stack
Storing data
Analyzing data
Acting on data
Misusing MLflow To Help Deduplicate Data At ScaleDatabricks
At Intuit, we have a lot of data – and a lot of duplicate data collected over decades. So we built a rule-based, self-serve tool to identify and merge duplicate records. It takes experimentation and iteration to get deduplication just right for 100s of millions of records, and spreadsheet-based tracking just wasn’t enough. We now use MLflow to automatically capture execution notes, rule settings, weights, key validation metrics, etc., all without requiring end-user action. In this talk, we’ll talk about our use case and why MLflow is useful outside its traditional ML Ops use cases.
Short introduction to different options for ETL & ELT in the Cloud with Microsoft Azure. This is a small accompanying set of slides for my presentations and blogs on this topic
Watch this recorded demonstration of SnapLogic from our team of experts who answer your hybrid cloud and big data integration questions.
demo, ipaas, elastic integration, cloud data, app integration, data integration, hybrid could integration, big data, big data integration
Our post recession economy is demanding better decision making in a more timely and effective manner. Business Intelligence (BI) software is the next tool you can't do without! From financial reporting to budgeting, company consolidation to sales analysis, we'll show you creative and powerful ways to utilize Sage's BI tools. If you're looking for a software package that is going to provide you the information you need, when you need it, in a format you can understand, then you simply must attend this session.
Let's discuss Elastic solutions that create simple OOB search experience whether you want to add a search box to your application or search across multiple data repositories within your organization. Presented by Artem Pogossian, Solutions Architect at Elastic.
From Spark to Ignition: Fueling Your Business on Real-Time AnalyticsSingleStore
What’s in Store For This Presentation?
1. MemSQL: A real-time database for transactions and analytics
2. Spark Use Cases
3. Example: Geospatial Enhancements
Effective AIOps with Open Source Software in a WeekDatabricks
Classic event, incident, problem and change management are ITSM practices that are getting integrated with DevOps/SRE and ML through a competency known as AIOps. Large streams of data generated through logs, metrics and traces are organized and computed using machine learning algorithms to extract insights on the anomalies of system behavior that could be impacting end-users and business transactions. Businesses cannot afford to see their end-users impacted by those anomalies and therefore would want to proactively predict the likelihood of systems regressing and take corrective action long before any material impact.
In this talk, we show the use of simple linear regression and multivariate linear regression techniques to predict the likelihood of system behavior resulting in one or two sigma of standard deviation. We show how to use FOSS tools to predict them using various decision trees that are integrated to high performing streaming platforms like Apache Flink, Apache Beam, Prometheus and Grafana which makes it a lot easier to visualize the various alerts and triage their way back to performing root cause analysis. These high performing systems are also backed by KAFKA for its streaming and distributed computing capabilities by partitioning the data for various staged analysis some of which can be done in parallel and concurrently based on the use cases. We present a fully integrated architecture that helps you realize a commercial AIOps capability without having to license expensive software products. The above open architecture allows you to implement various ML algorithms as needed and its agnostic to programming languages and tools.
The talk will combine various techniques with demos and is focused to practicing engineers and developers who are familiar with ML.
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Databricks
Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.
This presentation is to understand StreamSets ETL tool.
StreamSets is modern ETL tool designed to process streaming data.
StreamSets has 2 engines, 1 is Data Controller and Data Transformer(Based on Apache Spark).
Learn to Use Databricks for the Full ML LifecycleDatabricks
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
CTO View: Driving the On-Demand Economy with Predictive AnalyticsSingleStore
In the on-demand economy real-time analytics is both a necessity and a competitive advantage. The next evolution in the on-demand economy is in predictive analytics fueled by live streams of data—in effect knowing what customers want before they do. This session will feature technical examples of real-time pipelines, machine learning, and custom dashboards as well as off-the-shelf dashboards with Tableau.
Load data from Servicenow to Snowflake in minutessyed_javed
Modern data solution like Lyftron prebuilt connectors automatically deliver data to Snowflake warehouses in normalized, ready-to-query schemas and provide full search on data catalog.
Short introduction to different options for ETL & ELT in the Cloud with Microsoft Azure. This is a small accompanying set of slides for my presentations and blogs on this topic
Watch this recorded demonstration of SnapLogic from our team of experts who answer your hybrid cloud and big data integration questions.
demo, ipaas, elastic integration, cloud data, app integration, data integration, hybrid could integration, big data, big data integration
Our post recession economy is demanding better decision making in a more timely and effective manner. Business Intelligence (BI) software is the next tool you can't do without! From financial reporting to budgeting, company consolidation to sales analysis, we'll show you creative and powerful ways to utilize Sage's BI tools. If you're looking for a software package that is going to provide you the information you need, when you need it, in a format you can understand, then you simply must attend this session.
Let's discuss Elastic solutions that create simple OOB search experience whether you want to add a search box to your application or search across multiple data repositories within your organization. Presented by Artem Pogossian, Solutions Architect at Elastic.
From Spark to Ignition: Fueling Your Business on Real-Time AnalyticsSingleStore
What’s in Store For This Presentation?
1. MemSQL: A real-time database for transactions and analytics
2. Spark Use Cases
3. Example: Geospatial Enhancements
Effective AIOps with Open Source Software in a WeekDatabricks
Classic event, incident, problem and change management are ITSM practices that are getting integrated with DevOps/SRE and ML through a competency known as AIOps. Large streams of data generated through logs, metrics and traces are organized and computed using machine learning algorithms to extract insights on the anomalies of system behavior that could be impacting end-users and business transactions. Businesses cannot afford to see their end-users impacted by those anomalies and therefore would want to proactively predict the likelihood of systems regressing and take corrective action long before any material impact.
In this talk, we show the use of simple linear regression and multivariate linear regression techniques to predict the likelihood of system behavior resulting in one or two sigma of standard deviation. We show how to use FOSS tools to predict them using various decision trees that are integrated to high performing streaming platforms like Apache Flink, Apache Beam, Prometheus and Grafana which makes it a lot easier to visualize the various alerts and triage their way back to performing root cause analysis. These high performing systems are also backed by KAFKA for its streaming and distributed computing capabilities by partitioning the data for various staged analysis some of which can be done in parallel and concurrently based on the use cases. We present a fully integrated architecture that helps you realize a commercial AIOps capability without having to license expensive software products. The above open architecture allows you to implement various ML algorithms as needed and its agnostic to programming languages and tools.
The talk will combine various techniques with demos and is focused to practicing engineers and developers who are familiar with ML.
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Databricks
Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.
This presentation is to understand StreamSets ETL tool.
StreamSets is modern ETL tool designed to process streaming data.
StreamSets has 2 engines, 1 is Data Controller and Data Transformer(Based on Apache Spark).
Learn to Use Databricks for the Full ML LifecycleDatabricks
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
CTO View: Driving the On-Demand Economy with Predictive AnalyticsSingleStore
In the on-demand economy real-time analytics is both a necessity and a competitive advantage. The next evolution in the on-demand economy is in predictive analytics fueled by live streams of data—in effect knowing what customers want before they do. This session will feature technical examples of real-time pipelines, machine learning, and custom dashboards as well as off-the-shelf dashboards with Tableau.
Load data from Servicenow to Snowflake in minutessyed_javed
Modern data solution like Lyftron prebuilt connectors automatically deliver data to Snowflake warehouses in normalized, ready-to-query schemas and provide full search on data catalog.
Your Agile, Modern Data Delivery Platformsyed_javed
Lyftron eliminates traditional ETL/ELT bottlenecks with automatic data pipeline and make data instantly accessible to BI user with the modern cloud compute of Spark & Snowflake.
Lyftron connectors automatically convert any source into normalized, ready-to-query relational format and provide search capability on your enterprise data catalog.
Any data source becomes an SQL Query with all the power of
Apache Spark. Querona is a virtual database that seamlessly connects any data source with Power BI, TARGIT, Qlik, Tableau, Microsoft Excel or others. It lets you build your
own universal data model and share it among reporting tools.
Querona does not create another copy of your data, unless you want to accelerate your reports and use build-in execution engine created for purpose of Big Data analytics. Just write standard SQL query and let Querona consolidate data on the fly, use one of execution engines and accelerate processing no matter what kind and how many sources you have.
Why Business Intelligence Should Consider Agile Modern Data Delivery Platformsyed_javed
Modern data solution like Lyftron provides high availability and concurrency at all scales for modern analytical and business intelligence applications such as Looker, Tableau, PowerBI, Sisence, PeriscopeData etc. and can deliver timely results for you.
IBM Cloud Pak for Data is a single unified platform which helps to unify and simplify the collection, organization and analysis of data. Enterprises can turn data into insights through an integrated cloud-native architecture. IBM Cloud Pak for Data is extensible, easily customized to unique client data and AI landscapes through an integrated catalog of IBM, open source and third-party microservices add-ons
Why IT Should Consider Agile Modern Data Delivery Platformsyed_javed
Modern data delivery platform like Lyftron modernizes IT departments by offering them the flexibility to consolidated data of their choice without being bound by the structure or schema of the data warehouse.
Lyftrondata enables enterprises to load data from 300+ connectors to Google Bigquery in minutes without any engineering requirements. Simply connect, organize, centralize and share your data on Bigquery with zero code data pipeline, ETL & ELT tool.
Why HR Should Consider Agile Modern Data Delivery Platformsyed_javed
Modern data delivery platform like Lyftron provides universal data model capability to HR departments that enables changes from the source dynamically in the semantic layer and allows enterprises to avoid manual semantic data model changes.
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
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Mainframe Modernization with Precisely and Microsoft AzurePrecisely
Today’s businesses are leveraging Microsoft Azure to modernize operations, transform customer experience, and increase profit. However, if the rich data generated by the mainframe applications is missed in the move to the cloud, you miss the mark.
Without the right solutions in place, migrating mainframe data to Microsoft Azure is expensive, time-consuming, and reliant on highly specialized skillsets. Precisely Connect can quickly integrate mainframe data at scale into Microsoft Azure without sacrificing functionality, security, or ease of use.
View this on-demand webinar to hear from Microsoft Azure and Precisely data integration experts. You will:
- Learn how to build highly scalable, reliable data pipelines between the mainframe and Microsoft Azure services
- Understand how to make your Microsoft Azure implementation ready for mainframe
- Dive into case studies of businesses that have successfully included mainframe data in their cloud modernization efforts with Precisely and Microsoft Azure
My Slidedeck about Common Data Service and Model. This technology is under development so content is subject to change and based on current service on 4/13/2018
Streaming IBM i to Kafka for Next-Gen Use CasesPrecisely
Your team is always under pressure to accelerate the adoption of the most modern and powerful technologies. Simultaneously, your existing investments, such as IBM i, your organization’s most critical data asset, remain in a silo. The only practical path forward is to connect the new and existing with a streaming technology like Apache Kafka to feed real-time applications that power use cases ranging from marketing and order replenishment to fraud detection.
Join this Precisely webinar to learn how to unlock the potential of your IBM i data by creating data pipelines that integrate, transform, and deliver it to users when and where they need it. Additionally, hear how Stark Denmark, uses Precisely Connect CDC to provide data to their organization in real-time.
Join this webinar to:
- Understand the benefits and challenges of building data pipelines that access and integrate data from IBM i systems to modern data platforms
- Learn how Precisely can help you build real-time data pipelines
- Hear from Stark Denmark on how they are using Connect CDC from Precisely and the benefits they are getting
Microsoft® SQL Server® 2012 is a cloud-ready information platform that will help organizations unlock breakthrough insights across the organization and quickly build solutions to extend data across on-premises and public cloud, backed by mission critical confidence.
Similar to Load data from xml to Snowflake in minutes (20)
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
1. Lyftron enables realtime streaming and bulk loading on
Snowflake & accelerate data movement with the power of Spark
compute.
LYFTRON AND SNOWFLAKE
WHY LYFTRON
We will enable your business to make data-driven decisions by:
Empowering your business
with cost-effective, scalable
data solutions
Enabling quering any data
with ANSI SQL Pre built connectors
automatically deliver data
to warehouses in
normalized schemas
Shorten time to insights
and cut the data
management process by
75%
Eliminating complexity so you
can access your data easily
Load data
from xml
to Snowflake
in minutes
USE CASES WE SOLVE
Create data pipelines in minutes
Shorten time to insights
Enhanced security built for the cloud
Data lineage and replication
Query any data with ANSI SQL
Data warehouse modernization
Governed data lake(Data Vault 2.0)
Hybrid cloud management