Sabre Poland的动态

I love reading about customer wins like this one from JetBlue. Check out the case study below to find out how data observability helped their data team increase JetBlue's data NPS score 16 points Y-o-Y and drive data trust at scale across 3,400+ analyst tables. No time to read? Here are a couple quotes that stuck out: “Before we onboarded Monte Carlo, the data operations team’s primary focus was to just monitor pipeline runs that failed. But now, they’ve got this extra level of insight where they’re able to say ‘something is wrong with this table’ rather than just ‘something is wrong with this pipeline.’ In some cases, we see that the pipeline works fine and everything is healthy, but the data in the table itself is incorrect. Monte Carlo helps us see these issues, which we didn’t previously have a way to easily visualize.” Ashley Van Name, Senior Manager of Data Engineering at JetBlue. “Data trust is a human emotion, which isn’t always entirely rational. But if you can give the analysts access to Monte Carlo where they can see the health of all the data objects they are using, you can sell them [on the data’s quality].” Brian Pederson, Manager of Data Products at JetBlue Read the full story:?https://lnkd.in/dnAh8Y5K #dataobservability #montecarlo #data #jetblue

What is time-travel feature and how to use it in Databricks? In the dynamic world of data management, accessing historical data snapshots can be complex. Delta Lake's Time Travel feature simplifies this process, allowing users to journey through data history effortlessly. ???????? ???????????? ?????? ???????? ????????????????: Simplifying Data Exploration Imagine analyzing your company's marketing campaigns from the past year. Traditionally, this task involves piecing together disparate data snapshots— a complex and time-consuming process. Delta Lake's Time Travel feature changes the game. It lets users traverse different data versions seamlessly, leveraging the transaction log for precision. ?????? ?????????? ???? ???????? ????????????: 1?? ???????????? ???????????????????? ????????????????: Time Travel streamlines trend analyses and retrospective audits. It eliminates manual data snapshotting, making data exploration effortless. 2?? ?????????????????? ????????????????-????????????: With Time Travel, organizations make informed decisions. Accessing historical data with precision ensures clarity and mitigates risks associated with outdated information. #deltalake #dataengineering #WhatsTheData

Excited to share my recent group project designing comprehensive Data Warehouse for airline company with my team that was a very collaborative one and it was really great opportunity for me to work with them Abdelrahman Ahmed, Rahaf Mohamad, Manar Ayman . This project aimed to analyze flight activity, frequent flyers programs, reservations, and customer interactions to drive strategic decision-making. ?????????????? ??????????: Analyzed flight activity, reservations, and customer interactions to drive strategic decision-making. ?????? ????????????????????????: -Modeling processes for each business aspect. -Construction of logical and physical data models. -Population of sample data in Oracle DBMS. -Development of SQL queries for decision support. -Report on indexes and partitions in Data Warehousing. ????????????????: Utilized the Kimball model for dimensional modeling, ensuring relevance and impact in our insights. ???????????? ??????????: -Defined business processes, facts and grain for various processes. -Developed dimensions including Date, Interaction, and Passenger Profile. -Analyzed Flight Activity, Reservations, and Customer Care Interactions. ???????? ????????????????????: We've populated data for all facts and dimensions using Python scripts, ensuring our models are robust and ready for analysis. ???????????????? ??????????????: -Retrieve passenger details, including booking channel and revenue. -Obtain flight information such as airports, dates, and average delays. -Analyze passenger interactions, sentiments, and ratings. -Identify the top 5 airports with the highest departure counts. Feel free to check the project details via my GitHub repo:https://lnkd.in/gB7YVNVg #BigData #DataAnalytics #DataWarehousing #BusinessIntelligence #DecisionMaking #Analytics

OBSERVABILITY TIP: Where to send your application OpenTelemetry data? Send it a modern observability platform that can: -Visually organize data to easily pivot from Metrics, to Traces, to Logs? -Dynamically Connect all entities of deployments -Correlate OTEL data to Business transactions -Analyze OTEL data to create baselines -Create intuitive workflows to speed up Root Cause Analysis -Automatically surface anomolies and/or saturations -Apply datascience and machine learning to the OTEL data -Enable different personas to answer their unique questions -Mine the meta-data for Analytical Dashboards -Add more! Send your OTEL data to a modern platform which makes it easier to visualize, understand, analyze your applications.

Reading up on data visualization techniques with Agoda on Medium this morning! It's always important to review the basics to ensure our visualizations are telling the right stories. #dataanalytics #datavisualization #techcareerskills #informationscience #mediumarticle

It has now been 30 years ago…… It has now been 30 years ago since Knowledge Graph was used.?It was used by BI companies to build better Dashboards. Today 30 years later Gartner recommends that data and analytics leaders take a semantic approach to their enterprise data to drive business value and break through data silos. Democratizing data and gaining insights has never been more important to gaining a competitive advantage. Whether it's advanced analytics for decision making or modeling complex relationships from data that is too vast and too big to describe people, places, things and their relationships, knowledge graphs are changing the way information is found, used and leveraged. We may not realize it, but we use a knowledge graph when we search Google for an Animal, you get so many additional information regarding this Animal like books, events, movies, places, etc. Amazon and Netflix using Knowledge Graph since years. Airlines are a perfect match for Knowledge Graph. Flights going all over the world and tons of data available like: Source Airport, Destination Airport, Flight, City, Airline, Country and many more. All those dates have a relationship so perfect to build a graph model out of this. If you book your flight, you get all the information based on Knowledge Graph’s you are able to sort your results. Cheapest flight from A to B, Non-Stop or many stops, Connections flights, how long is the flight and many more. Democratizing big data management through a semantic layer As it stands now, most enterprise data is stored in a data lake, which contains all the available data (variety, volume) in a centralized, commodity storage. One pitfall of relying on information contained in a data lake is that the high volume of information from a variety of sources makes it difficult to know where it is and where it came from and what it means. In other words it is difficult for data consumers to understand the context of the data. In turn, this makes it difficult to trust its veracity as well as put it to good use. For more information, go to https://lnkd.in/eQXY6Rsd

In today’s episode of ‘Cool stuff from Altair’:

Creating a robust reporting system requires three fundamental pillars: data, content, and tools. Senior Data Product Manager at CWT, Logan LaBonne, highlights that when these elements work together seamlessly, even at a basic level, they can generate significant insights and actions. From managing operational costs to ensuring traveler well-being and sustainability, the right data analysis can drive better decision-making. Start with the basics and lay the groundwork for advanced analytics. https://bit.ly/3LU3AJu #Analytics #TravelManagement #DataStrategy #DataTools #BusinessTravel

Creating a robust reporting system requires three fundamental pillars: data, content, and tools. Senior Data Product Manager at CWT, Logan LaBonne, highlights that when these elements work together seamlessly, even at a basic level, they can generate significant insights and actions. From managing operational costs to ensuring traveler well-being and sustainability, the right data analysis can drive better decision-making. Start with the basics and lay the groundwork for advanced analytics. https://bit.ly/3LU3AJu #Analytics #TravelManagement #DataStrategy #DataTools #BusinessTravel

A robust reporting system relies on three pillars: data, content, and tools. Logan LaBonne, Senior Data Product Manager at CWT, emphasizes that even basic integration of these can yield significant insights. Effective data analysis helps manage costs, ensure traveler well-being, and drive better decisions. #Analytics #TravelManagement #DataStrategy #DataTools