CIO
SEPTEMBER 14, 2023
Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. Database-centric: In larger organizations, where managing the flow of data is a full-time job, data engineers focus on analytics databases. What is a data engineer?
CIO
NOVEMBER 9, 2022
One potential solution to this challenge is to deploy self-service analytics, a type of business intelligence (BI) that enables business users to perform queries and generate reports on their own with little or no help from IT or data specialists. But there are right and wrong ways to deploy and use self-service analytics.
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CIO
AUGUST 9, 2022
Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. Database-centric: In larger organizations, where managing the flow of data is a full-time job, data engineers focus on analytics databases. What is a data engineer? Data engineer job description.
Dzone - DevOps
MARCH 18, 2024
Incorporating AI into API and microservice architecture design for the Cloud can bring numerous benefits. Predictive analytics : AI can leverage historical data to predict usage trends, identify potential bottlenecks, and offer proactive solutions for enhancing the scalability and reliability of APIs and microservices.
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Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind. 9 questions to ask yourself when planning your ideal architecture.
Dzone - DevOps
JANUARY 23, 2024
Companies across various industries rely on big data analytics to gain valuable insights and make informed business decisions. To efficiently process and analyze this vast amount of data, organizations need a robust and scalable architecture.
Datavail
NOVEMBER 17, 2021
As data volumes continue to grow, the systems and architectures need to evolve. This is especially important for companies that rely on analytics to drive business insights and executive decisions. Most likely, your company has shifted their approach to data and analytics. Bringing Modern Analytics Solutions to the Table.
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Embedding analytics in your application doesn’t have to be a one-step undertaking. Read more about how to simplify the deployment and scalability of your embedded analytics, along with important considerations for your: Environment Architecture: An embedded analytics architecture is very similar to a typical web architecture.
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Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital.
Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri
Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases. Yet they are continually challenged with providing access to all of their data across business units, regions, and cloud environments.
Speaker: Veena Vasudevan and Jason Hughes
With data stored in vendor-agnostic files and table formats like Apache Iceberg, the open lakehouse is the best architecture to enable data democratization. By moving analytic workloads to the data lakehouse you can save money, make more of your data accessible to consumers faster, and provide users a better experience.
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Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data. The primary architectural principles of a true cloud data lake, including a loosely coupled architecture and open file formats and table structures.
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Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture. The decision making around this transition.
Speaker: Ahmad Jubran, Cloud Product Innovation Consultant
Many do this by simply replicating their current architectures in the cloud. Those previous architectures, which were optimized for transactional systems, aren't well-suited for the new age of AI. In this webinar, you will learn how to: Take advantage of serverless application architecture. And much more!
Speaker: speakers from Verizon, Snowflake, Affinity Federal Credit Union, EverQuote, and AtScale
Driving a self-service analytics culture with a semantic layer. Using predictive/prescriptive analytics, given the available data. Avoiding common analytics infrastructure and data architecture challenges. The impact that data literacy programs and using a semantic layer can deliver.
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