Knowledge Hut

DECEMBER 21, 2023

What is Data Engineering? Data engineering is the method to collect, process, validate and store data. It involves building and maintaining data pipelines, databases, and data warehouses. The purpose of data engineering is to analyze data and make decisions easier.

Data Engineering 52

Data Engineering Data Engineer Engineering MongoDB 52

Knowledge Hut

MARCH 22, 2024

Role Level: It is a mid-level and requires moderate experience and expertise in cloud architecture. Automate deployment processes and streamline workflows. Cloud Consultant A Cloud Consultant helps businesses migrate to cloud computing systems via the process of adopting and optimizing the cloud for their business needs.

AWS 52

AWS Amazon Web Services Cloud Computing Google Cloud 52

Knowledge Hut

NOVEMBER 17, 2023

Data Architecture and Design: These experts excel in creating effective data structures that meet scalability requirements, ensure optimal data storage, processing, and retrieval, and correspond with business demands. Azure Data Factory stands at the forefront, orchestrating data workflows.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Monte Carlo

MARCH 9, 2023

Some of the leading data teams have discovered ways to do exactly that by leveraging data observability to automatically monitor and alert when accuracy levels dip below acceptable standards. That is what JetBlue did as described by data scientist Derrick Olson in a recent Snowflake webinar. JetBlue’s data architecture.

Engineering 52

Engineering Data Pipeline Machine Learning Data Science 52

Knowledge Hut

MARCH 26, 2024

Role Level: Senior-level position requiring extensive experience in enterprise architecture and a deep understanding of business processes. Responsibilities: Develop comprehensive architecture blueprints that align with business objectives and IT strategies. There are plenty of remote solution architect jobs available as well.

Amazon Web Services 52

Amazon Web Services Google Cloud Computer Science AWS 52

Knowledge Hut

NOVEMBER 17, 2023

Natural Language Processing (NLP): For those interested in NLP, knowledge of NLP libraries such as NLTK, spaCy, and GPT-3 is essential. Computer Vision: If computer vision is your focus, understanding OpenCV and image processing techniques is a must. This includes proficiency in areas such as linear algebra, calculus, and probability.

Deep Learning 52

Deep Learning Data Science Machine Learning Certification 52

Cloudera

DECEMBER 19, 2018

Business intelligence (BI), an umbrella term coined in 1989 by Howard Dresner, Chief Research Officer at Dresner Advisory Services, refers to the ability of end-users to access and analyze enterprise data. In 2015, only 17% of organizations surveyed had big data implementations. Natural Language Analysis and Streaming Data Analytics.

BI 52

BI Cloud Computing Data Warehouse Big Data 52

Cloudera

DECEMBER 28, 2017

With this in mind, Nordea implemented a modern data architecture based on Cloudera that allowed them to improve data quality, cut data ingest times by 87%, shorten DevOps cycle times, and simplify data management processes. Click here to listen to the full webinar with Nordea.

Banking 40

Banking Data Ingestion Big Data Data Architecture 40

Knowledge Hut

SEPTEMBER 29, 2023

Microsoft Data Engineer Certification is one such certification which is most sought after by professionals. By combining data from various structured and unstructured data systems into structures, Microsoft Azure Data Engineers will be able to create analytics solutions.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

MARCH 28, 2024

What are the components of data-as-a-product? A data product, coined by DJ Patil, Former Chief Data Scientist of the United States, has several components, including a product management process, the domain wrapper comprising a semantic layer, business logic and metrics, and access.

Data 52

Data High Quality Data Machine Learning Accessible 52

Knowledge Hut

NOVEMBER 28, 2023

The contemporary world experiences a huge growth in cloud implementations, consequently leading to a rise in demand for data engineers and IT professionals who are well-equipped with a wide range of application and process expertise. Work closely with software engineers and data scientists. Technical Data Engineer Skills 1.Python

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Be sure to read to the end where we share 6 data pipeline architecture diagrams used by real data teams at companies like JetBlue, Fox Networks, Drata, and more. What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Cloudera

AUGUST 30, 2022

Modern data architectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern data architectures (MDAs). Solutions that support MDAs are purpose-built for data collection, processing, and sharing.

Data Architecture 76

Data Architecture Architecture Data Lake NoSQL 76

Monte Carlo

NOVEMBER 30, 2022

During IMPACT 2022: The Data Observability Summit, I had the distinct privilege of chatting with three data pros that have direct experience on how to implement data mesh architectures: Max Schultze at fashion and shoe retailer Zalando, Matheus Espanhol at technology solutions provider BairesDev, and Samia Rahman at biotech startup Seagen.

Data 52

Data Government Architecture Data Lake 52

Monte Carlo

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Conduct Pre-Mortems 38.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Conduct Pre-Mortems 38.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

DataKitchen

DECEMBER 30, 2020

In June of 2020, CRN featured DataKitchen’s DataOps Platform for its ability to manage the data pipeline end-to-end combining concepts from Agile development, DevOps, and statistical process control: DataKitchen. DBTA Big Data Quarterly’s Big Data 50—Companies Driving Innovation in 2020.

Manufacturing 96

Manufacturing Big Data Data Pipeline Data Science 96

DataKitchen

JUNE 1, 2022

Is my dashboard displaying the correct data? Is the delay because the job started late, or did it take too long to process? They also can’t answer a similar set of questions about their development process: Deploys. They have a Low Change Appetite: Teams have complicated in place data architectures and tools.

Management 52

Management Data Data Analytics Data Architecture 52

Cloudera

SEPTEMBER 8, 2021

CDOs are under increasing pressure to reduce costs by moving data and workloads to the cloud, similar to what has happened with business applications during the last decade. Our upcoming webinar is centered on how an integrated data platform supports the data strategy and goals of becoming a data-driven company.

Cloud Computing 75

Cloud Computing Government Data Lake Cloud 75

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. The biggest challenge is broken data pipelines due to highly manual processes. Figure 1 shows a manually executed data analytics pipeline. When can you declare it done?

Engineering 60

Engineering BI Business Analyst Metadata 60

Cloudera

NOVEMBER 27, 2023

Flink Forward is a great opportunity to learn about the cutting edge of streaming and stream processing technologies. Many critical downstream applications consume data processed by Flink, especially telcos, financial services, and e-commerce, where real-time processing needs are pronounced. Takeaway No. Takeaway No.

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Kafka SQL ETL Tools Data Lake 80