Upsert your datasets using the Append tool in ArcGIS Pro 3.1
ArcGIS
FEBRUARY 27, 2023
you can use the Append tool to upsert (update and insert) a target dataset with data from a new or updated dataset. In ArcGIS Pro 3.1,
Beyond Garbage Collection: Tackling the Challenge of Orphaned Datasets
Ascend.io
MAY 23, 2023
A prime example of such patterns is orphaned datasets. These are datasets that exist in a database or data storage system but no longer have a relevant link or relationship to other data, to any of the analytics, or to the main application — making them a deceptively challenging issue to tackle.
Datasets 
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Webinars
How to Optimize the Developer Experience for Monumental Impact
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Understanding User Needs and Satisfying Them
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know
Leading the Development of Profitable and Sustainable Products
Strategies And Tactics For A Successful Master Data Management Implementation
Data Engineering Podcast
JUNE 26, 2022
Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Master Data Management (MDM) is the process of building consensus around what the information actually means in the context of the business and then shaping the data to match those semantics.
Data Management 100
100 Webinars
How to Optimize the Developer Experience for Monumental Impact
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Understanding User Needs and Satisfying Them
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know
Leading the Development of Profitable and Sustainable Products
Why Your Master Data Management Needs Data Governance
Precisely
SEPTEMBER 5, 2023
With cloud computing, the capacity to extract value from data is greater than ever. As this realization grows, businesses are shifting their investments from hardware to technologies that optimize data assets. Master Data Management systems (MDM) play an important role in harmonizing data assets across large and midsize enterprises.
Terrain datasets as ground—Create scenes in ArcGIS Pro and publish to ArcGIS Online
ArcGIS
NOVEMBER 8, 2023
Terrain datasets as ground—Create scenes in ArcGIS Pro and publish to ArcGIS Online
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The Ultimate Fivetran Alternative: A Football-Inspired Approach to Data Management
Ascend.io
AUGUST 15, 2023
This same principle holds true in data management. It’s no surprise, then, that the quest for Fivetran alternatives is on the rise as organizations set their sights on a more holistic data approach. Defense: Saving Money with Intelligent Data Refresh In football, a solid defense does more than just stop goals.
Terrain datasets—Surfaces in a centralized location
ArcGIS
NOVEMBER 8, 2023
Surfaces in a centralized location
Datasets 67
67 
Breaking Down Data Silos in Financial Services with a Centralized Data Management Platform
Cloudera
APRIL 25, 2019
As well as managing the UK’s currency, supply of money and interest rates, the institute has a diverse range of responsibilities including gathering and analyzing data from banks, building societies, credit unions, insurers and mortgage companies to inform policy decisions and guide UK government departments and international organizations.
Simplifying BI pipelines with Snowflake dynamic tables
ThoughtSpot
MARCH 5, 2024
While AI-powered, self-service BI platforms like ThoughtSpot can fully operationalize insights at scale by delivering visual data exploration and discovery, it still requires robust underlying data management. Snowflake's new dynamic tables feature redefines how BI and analytics teams approach data transformation pipelines.
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Deciphering the Data Enigma: Big Data vs Small Data
Knowledge Hut
APRIL 23, 2024
Big Data vs Small Data: Volume Big Data refers to large volumes of data, typically in the order of terabytes or petabytes. It involves processing and analyzing massive datasets that cannot be managed with traditional data processing techniques.
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6 Pillars of Data Quality and How to Improve Your Data
Databand.ai
MAY 30, 2023
Methods: Enhancing data quality might involve cleansing, standardizing, enriching, or validating data elements, while preserving data integrity necessitates robust access controls, encryption measures, and backup/recovery strategies. Learn more in our detailed guide to data reliability 6 Pillars of Data Quality 1.
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Data Testing Tools: Key Capabilities and 6 Tools You Should Know
Databand.ai
AUGUST 30, 2023
Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content. Improved Data Quality The primary goal of using data testing tools is to enhance the overall quality of an organization’s data assets.
Data Cleanse 52
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Building a Winning Data Quality Strategy: Step by Step
Databand.ai
AUGUST 30, 2023
This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Additionally, high-quality data reduces costly errors stemming from inaccurate information.
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Difference Between Data Structure and Database
Knowledge Hut
MARCH 27, 2024
Essential in programming for tasks like sorting, searching, and organizing data within algorithms. Examples MySQL, PostgreSQL, MongoDB Arrays, Linked Lists, Trees, Hash Tables Scaling Challenges Scales well for handling large datasets and complex queries. Flexibility: Offers scalability to manage extensive datasets efficiently.
Database 52
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A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems
Towards Data Science
FEBRUARY 6, 2024
Operational data management in Data Mesh A Data Mesh implementation improved my experience in these aspects: Knowledge : I could quickly identify the owners of the exposed data. The distance between the owner and the domain that generated the data is key to expedite further analytical development.
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Cloudera Customer Story
Cloudera
DECEMBER 13, 2023
He is responsible for introducing common tools and processes and enhancing the technology, tools and data available to employees, empowering them to use their expertise and creativity in innovative ways. LGIM’s data ecosystem had become fragmented over time as it had grown.
The Five Use Cases in Data Observability: Overview
DataKitchen
MAY 10, 2024
Data ingestion observability helps organizations avoid costly mistakes when poor-quality data is used in critical business processes. Production During the production cycle, it’s crucial to oversee processes involving multiple tools and datasets, such as dashboard production or warehouse building.
How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions
KDnuggets
AUGUST 22, 2019
As machine learning evolves, the need for tools and platforms that automate the lifecycle management of training and testing datasets is becoming increasingly important.
Be Confident In Your Data Integration By Quickly Validating Matching Records With data-
Data Engineering Podcast
JULY 3, 2022
In this episode they explain how the utility is implemented to run quickly and how you can start using it in your own data workflows to ensure that your data warehouse isn’t missing any records from your source systems. Random data doesn’t do it — and production data is not safe (or legal) for developers to use.
Data Integration 100
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History of Big Data
Knowledge Hut
APRIL 23, 2024
Gradually, data storage and processing systems evolved, and today, we see it in one of its most advanced forms, the cloud. Early Challenges and Limitations in Data Handling The history of data management in big data can be traced back to manual data processing—the earliest form of data processing, which makes data handling quite painful.
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Who is a Big Data Engineer? Skills, Responsibilities, Salary
Knowledge Hut
MARCH 13, 2024
The big data engineer then analyzes this data using unique algorithms and data models to gain valuable insights. What Does A Big Data Engineer Do? Roles and Responsibilities] What does a big data engineer do? A big data engineer is crucial to any company’s data management team.
Big Data 52
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Who is a Big Data Engineer? Skills, Responsibilities, Salary
Knowledge Hut
MARCH 13, 2024
The big data engineer then analyzes this data using unique algorithms and data models to gain valuable insights. What Does A Big Data Engineer Do? Roles and Responsibilities] What does a big data engineer do? A big data engineer is crucial to any company’s data management team.
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52 
Parcel Protection: Inside UPS Capital’s Defensive Strategy with Striim & Google
Striim
MAY 1, 2024
The sheer volume of data generated from the increasing package deliveries overwhelmed existing data management systems, underscoring a critical need for more advanced data handling capabilities. The absence of real-time data processing capabilities hindered UPS Capital’s risk management and rapid response efforts.
Google Cloud 52
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Big Data vs Traditional Data
Knowledge Hut
APRIL 23, 2024
Infrastructure Required to Manage Data Big data management can often prove to be complicated and expensive. Smaller and more cost-effective ways of managing data. Let us now take a detailed look into how Big Data differs from Traditional relational databases.
Big Data 52
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Top 10 Database Management Skills for Your Resume in 2024
Knowledge Hut
APRIL 23, 2024
Why are Database Management Skills Important? In the current digital era, data has become a crucial asset for businesses in all industries. Effective data management is essential for businesses to make informed decisions, enhance customer experiences, and acquire a competitive edge.
Database 52
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Intrinsic Data Quality: 6 Essential Tactics Every Data Engineer Needs to Know
Monte Carlo
JANUARY 10, 2024
Use of Data Quality Tools Refresh your intrinsic data quality with data observability 1. Data Profiling Data profiling is getting to know your data, warts and quirks and secrets and all. Through data profiling, you can uncover your data’s underlying patterns, relationships, and anomalies.
Data Cleanse 52
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Cleaning And Curating Open Data For Archaeology
Data Engineering Podcast
FEBRUARY 3, 2019
In this episode Eric Kansa describes how they process, clean, and normalize the data that they host, the challenges that they face with scaling ETL processes which require domain specific knowledge, and how the information contained in connections that they expose is being used for interesting projects. That’s one of the bottle necks.
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The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring
DataKitchen
MAY 10, 2024
This blog post explores the challenges and solutions associated with data ingestion monitoring, focusing on the unique capabilities of DataKitchen’s Open Source Data Observability software. This process is critical as it ensures data quality from the onset.
Solving Data Lineage Tracking And Data Discovery At WeWork
Data Engineering Podcast
DECEMBER 16, 2019
Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform.
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Top 8 Hadoop Projects to Work in 2024
Knowledge Hut
DECEMBER 28, 2023
Hadoop is a popular open-source framework that stores and processes large datasets in a distributed manner. Organizations are increasingly interested in Hadoop to gain insights and a competitive advantage from their massive datasets. Hadoop can store data and run applications on cost-effective hardware clusters.
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5 Layers of Data Lakehouse Architecture Explained
Monte Carlo
JANUARY 5, 2024
A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake. Innovations in data lakehouse architecture have been an important step toward more flexible and powerful data management systems. Image courtesy of Databricks.
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Data Lakehouse Architecture Explained:Â 5 Layers
Monte Carlo
JANUARY 5, 2024
A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake. Innovations in data lakehouse architecture have been an important step toward more flexible and powerful data management systems. Image courtesy of Databricks.
Architecture 52
52 
Add Version Control To Your Data Lake With LakeFS
Data Engineering Podcast
NOVEMBER 2, 2020
In this episode Einat Orr and Oz Katz explain how they implemented branching and merging capabilities for object storage, best practices for how to use versioning primitives to introduce changes to your data lake, how LakeFS is architected, and how you can start using it for your own data platform.
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100 Synthetic Data As A Service For Simplifying Privacy Engineering With Gretel
Data Engineering Podcast
APRIL 10, 2022
Summary Any time that you are storing data about people there are a number of privacy and security considerations that come with it. Privacy engineering is a growing field in data management that focuses on how to protect attributes of personal data so that the containing datasets can be shared safely.
Engineering 100
100 
How to Become Data Scientist in 2024 [Step-by-Step]
Knowledge Hut
DECEMBER 22, 2023
Big Data Technologies: Familiarize yourself with distributed computing frameworks like Apache Hadoop and Apache Spark. Learn how to work with big data technologies to process and analyze large datasets. Data Management: Understand databases, SQL, and data querying languages. Who can Become Data Scientist?
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Data Engineering Weekly #170
Data Engineering Weekly
MAY 5, 2024
The motivation for Machine Unlearning is critical from the privacy perspective and for model correction, fixing outdated knowledge, and access revocation of the training dataset. link] LinkedIn: LakeChime - A Data Trigger Service for Modern Data Lakes LinkedIn points out two critical flaws in a partitioned approach to data management.
Big Data Technologies that Everyone Should Know in 2024
Knowledge Hut
APRIL 25, 2024
These four fields are at the forefront of big data technology and are essential for understanding and managing large datasets. Top Big Data Technologies in 2024 As data becomes increasingly central to our lives, the need to effectively collect, store, and analyze it has never been greater.
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Best Data Science Books for Beginners and Experienced [2024]
Knowledge Hut
DECEMBER 26, 2023
Mining of Massive Datasets By Jure Leskovec, Anand Rajaraman, Jeff Ullma This book will provide a comprehensive understanding of large-scale data mining and network analysis. Some other features that make it a book ideal for advanced learners are as follows:- This book will help you learn how to mine significantly large datasets.
Data Science 52
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Data testing tools: Key capabilities you should know
Databand.ai
AUGUST 30, 2023
Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content. Improved data quality The primary goal of using data testing tools is to enhance the overall quality of an organization’s data assets.
Data Cleanse 52
52 
ELT Explained: What You Need to Know
Ascend.io
NOVEMBER 21, 2023
The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Text String Modifications: Editing and refining text strings for clarity and uniformity, essential for consistent data interpretation.
Raw Data 52
52 
Top 15 Power BI Project Ideas
Knowledge Hut
OCTOBER 27, 2023
Since we've all been students, working with a dataset you're familiar with makes it easier to learn. Start by getting a dataset and splitting it into different performance parts. Practice Dataset Student Result 5. Weather data is abundant, and it offers unique variations and patterns.
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52 Moving Machine Learning Into The Data Pipeline at Cherre
Data Engineering Podcast
APRIL 19, 2021
After listening to this you’ll look at your data pipelines in a new light and start to wonder how you can bring more advanced strategies into the cleaning and transformation process. Interview Introduction How did you get involved in the area of data management? Therefore, yielding similar, but completely different data.
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Prepare Your Unstructured Data For Machine Learning And Computer Vision Without The Toil Using Activeloop
Data Engineering Podcast
AUGUST 14, 2021
He discusses the inefficiencies that teams run into from having to reprocess data multiple times, his work on the open source Hub library to solve this problem for everyone, and his thoughts on the vast potential that exists for using computer vision to solve hard and meaningful problems. What do you have planned for the future of Activeloop?
Druid Deprecation and ClickHouse Adoption at Lyft
Lyft Engineering
NOVEMBER 29, 2023
In addition, our experimentation team utilized the datastore to analyze recent experimentation data. Managing Druid and Issues We Faced At Lyft, our primary Druid customers were data engineers and software engineers. For our time-series based datasets, many of the tables are sorted on event_time (or occurred_at time).
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