Evolution in ETL: How Skipping Transformation Enhances Data Management
KDnuggets
DECEMBER 12, 2023
This article provides an overview of two new data preparation techniques that enable data democratization while minimizing transformation burdens.
Tableau Prep Builder: Streamline Your Data Preparation Process
Edureka
JULY 5, 2024
Tableau Prep is a fast and efficient data preparation and integration solution (Extract, Transform, Load process) for preparing data for analysis in other Tableau applications, such as Tableau Desktop. simultaneously making raw data efficient to form insights.
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Agent Tooling: Connecting AI to Your Tools, Systems & Data
How to Modernize Manufacturing Without Losing Control
Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration
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Modern Data Management Essentials: Exploring Data Fabric
Precisely
JULY 18, 2024
Key Takeaways Data Fabric is a modern data architecture that facilitates seamless data access, sharing, and management across an organization. Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata.
No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically
DataKitchen
FEBRUARY 17, 2025
In The Land Of The Blind, The Data Engineer Who Has Data Quality Testing In Production Is King Data engineers experience burnout at alarming rates , with many considering leaving the industry or their current company within the following year.
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Build Your Second Brain One Piece At A Time
Data Engineering Podcast
APRIL 28, 2024
In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain. What are the features and focus of Pieces that might encourage someone to use it over the alternatives?
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Unlocking Generative AI ROI: It Starts with Your Data Strategy
Snowflake
APRIL 22, 2025
Even these early adopters, the ones who report great overall success, have hit some snags with their data platforms. At the data platform level, we found: 55% of organizations are hampered by time-consuming data management tasks such as labeling. 51% say data preparation is too hard.
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Leveraging Human Intelligence For Better AI At Alegion With Cheryl Martin - Episode 38
Data Engineering Podcast
JULY 1, 2018
Cheryl Martin, Chief Data Scientist for Alegion, discusses the importance of properly labeled information for machine learning and artificial intelligence projects, the systems that they have built to scale the process of incorporating human intelligence in the data preparation process, and the challenges inherent to such an endeavor.
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How DataOS Nails Gartner’s Magic Quadrant for Data Integration
The Modern Data Company
JANUARY 22, 2024
The Modern Story: Navigating Complexity and Rethinking Data in The Business Landscape Enterprises face a data landscape marked by the proliferation of IoT-generated data, an influx of unstructured data, and a pervasive need for comprehensive data analytics.
Bring Order To The Chaos Of Your Unstructured Data Assets With Unstruk
Data Engineering Podcast
JUNE 17, 2021
If you are wondering how to deal with all of the information that doesn’t fit in your databases or data warehouses, then this episode is for you. Can you describe what Unstruk Data is and the story behind it? What would you classify as "unstructured data"? What would you classify as "unstructured data"?
Designing For Data Protection
Data Engineering Podcast
NOVEMBER 11, 2019
Summary The practice of data management is one that requires technical acumen, but there are also many policy and regulatory issues that inform and influence the design of our systems. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council.
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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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How DataOS Nails Gartner’s Magic Quadrant for Data Integration
The Modern Data Company
JANUARY 22, 2024
The Modern Story: Navigating Complexity and Rethinking Data in The Business Landscape Enterprises face a data landscape marked by the proliferation of IoT-generated data, an influx of unstructured data, and a pervasive need for comprehensive data analytics.
Bringing Automation To Data Labeling For Machine Learning With Watchful
Data Engineering Podcast
AUGUST 13, 2022
In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine learning data preparation and how it allows data engineers to be involved in the process. Can you describe what Watchful is and the story behind it? What are your core goals at Watchful?
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Cloudera Named a Leader in the 2022 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS)
Cloudera
DECEMBER 16, 2022
We’re proud to be recognized for the data management and data analytics innovations we have delivered in the new Cloudera Data Platform (CDP). For example, Gartner has provided very pointed guidance on the urgency and inevitability of hybrid and multi-cloud for data management.
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Data Vault on Snowflake: Feature Engineering and Business Vault
Snowflake
MARCH 30, 2023
A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value. ML workflow, ubr.to/3EJHjvm
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Don’t Blink: You’ll Miss Something Amazing!
Cloudera
OCTOBER 4, 2023
Containerized service to run both multiple compute clusters against the same data, and to configure each cluster with its own unique characteristics (instance types, initial and growth sizing parameters, and workload aware auto scaling capabilities).
Data Scientist vs Data Engineer: Differences and Why You Need Both
AltexSoft
OCTOBER 30, 2021
A data scientist takes part in almost all stages of a machine learning project by making important decisions and configuring the model. Data preparation and cleaning. Final analytics are only as good and accurate as the data they use. Managing data and metadata.
An AI Chat Bot Wrote This Blog Post …
DataKitchen
DECEMBER 9, 2022
DataOps involves close collaboration between data scientists, IT professionals, and business stakeholders, and it often involves the use of automation and other technologies to streamline data-related tasks. One of the key benefits of DataOps is the ability to accelerate the development and deployment of data-driven solutions.
The Power of Location Data: Driving Business Value with Spatial Analytics
Precisely
SEPTEMBER 12, 2024
Companies want to spend less time on data preparation and more time deriving insights from easy-to-access location reports. Location data management and analytics : This solution is ideal if your organization needs to orchestrate large volumes of spatial data. Enabling competitive advantage.
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Achieving Trusted AI in Manufacturing
Cloudera
JANUARY 30, 2024
While AI stands to drive smart intelligent factories, optimize production processes, enable predictive maintenance and pattern analysis, personalization, sentiment analysis, knowledge management, as well as detect abnormalities, and many other use cases, without a robust data management strategy, the road to effective AI is an uphill battle.
Top Data Cleaning Techniques & Best Practices for 2024
Knowledge Hut
JANUARY 25, 2024
Data cleaning is like ensuring that the ingredients in a recipe are fresh and accurate; otherwise, the final dish won't turn out as expected. It's a foundational step in data preparation, setting the stage for meaningful and reliable insights and decision-making. Why Is Data Cleaning So Important?
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Data Testing Tools: Key Capabilities and 6 Tools You Should Know
Databand.ai
AUGUST 30, 2023
Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. There are several types of data testing tools. Trifacta Trifacta is a data testing tool that helps organizations prepare and transform their data for analysis.
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Spur Telecom Growth with Location Intelligence
Precisely
DECEMBER 4, 2023
To answer the three fundamental questions outlined above, telecoms rely on business-friendly GIS to create a single view of the network that’s accessible, easily understood, and trusted by internal stakeholders to drive better, data-informed decisions. They also need a strong foundation of data science to underpin those efforts.
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure
DataKitchen
JULY 27, 2023
Azure Synapse Analytics Pipelines: Azure Synapse Analytics (formerly SQL Data Warehouse) provides data exploration, data preparation, data management, and data warehousing capabilities. It provides data prep, management, and enterprise data warehousing tools. It does the job.
Power BI Skills in Demand: How to Stand Out in the Job Market
Knowledge Hut
SEPTEMBER 26, 2023
Adding slicers and filters to allow users to control data views. Data Preparation and Transformation Skills Preparing the raw data into the right structure and format is the primary and most important step in data analysis. You must be well versed in using the data dictionary tool in Power BI for this task.
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Business Intelligence Analyst Jobs
Knowledge Hut
JUNE 28, 2023
Outlined below are the key responsibilities of a senior business intelligence analyst: Gather, Clean, and Store Data The primary role of remote business intelligence analyst jobs or full-time positions is data management. So, the first and foremost thing to do is to gather raw data.
Data Lake vs. Data Warehouse: Differences and Similarities
U-Next
SEPTEMBER 7, 2022
On the other hand, a data warehouse contains historical data that has been cleaned and arranged. . What is Data Warehouse? . Built to make strategic use of data, a Data Warehouse is a combination of technologies and components. In other words, it is the process of converting data into information. .
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Has the Data Engineer replaced the Business Intelligence Developer?
Advancing Analytics: Data Engineering
JULY 2, 2019
The Data Science Engineer Let’s start with the original idea of the Data Engineer, the support of Data Science functions by providing clean data in a reliable, consistent manner, likely using big data technologies. I’m going to refer to this role as the Data Science Engineer to differentiate from its current state.
Cloudera & Informatica – Next-Gen Analytics Partners
Cloudera
MAY 16, 2019
The traditional Data Warehouse ETL process has splintered into many smaller components. Ingest is now focused data capture and real-time trend analysis where possible. Once data is brought under control in a system like Cloudera, then the work of Data Preparation, Quality begins.
What are the Features of Big Data Analytics
Knowledge Hut
APRIL 25, 2024
These technologies are necessary for data scientists to speed up and increase the efficiency of the process. The main features of big data analytics are: 1. Data wrangling and Preparation The idea of Data Preparation procedures conducted once during the project and performed before using any iterative model.
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Big Data Analytics: How It Works, Tools, and Real-Life Applications
AltexSoft
MAY 14, 2021
What is Big Data analytics? Big Data analytics is the process of finding patterns, trends, and relationships in massive datasets that can’t be discovered with traditional data management techniques and tools. The best way to understand the idea behind Big Data analytics is to put it against regular data analytics.
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Explore and Prepare Your Data with ArcGIS Pro Data Engineering
ArcGIS
MAY 30, 2023
Get a taste of how to use Data Engineering to explore, visualize, clean and prepare data in ArcGIS Pro.
What is Data Fabric: Architecture, Principles, Advantages, and Ways to Implement
AltexSoft
AUGUST 22, 2022
Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes. With these challenges in enterprise data management, there has to be an approach to overcoming them, right? The solution is called a data fabric.
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How to become Azure Data Engineer I Edureka
Edureka
FEBRUARY 7, 2023
An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.
Top 10 Azure Data Engineer Job Opportunities in 2024 [Career Options]
Knowledge Hut
MARCH 28, 2024
Data Engineer Career: Overview Currently, with the enormous growth in the volume, variety, and veracity of data generated and the will of large firms to store and analyze their data, data management is a critical aspect of data science. That’s where data engineers are on the go.
Start DataOps Today with ‘Lean DataOps’
DataKitchen
SEPTEMBER 20, 2021
Data professionals could do amazing things, but not while held back by bureaucratic processes. An organization is unlikely to surpass competitors and overcome obstacles while its highly trained data scientists are hand-executing data preparation, ingestion and other pipelines. . Measure DataOps Maturity.
New Snowflake Features Released in January 2023
Snowflake
FEBRUARY 13, 2023
Today, the company also leads the world in supplier management innovation with apexportal and smartvm, now the most widely used supplier onboarding and compliant master data management solution in global procure-to-pay processes.
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Case Study: The Path to Better Pollution Forecasting Goes Through Nested JSON
Rockset
MARCH 19, 2019
“With Rockset, I don’t have to worry about data being typed or formatted in a way I didn’t anticipate, and I don’t have to modify my code every time the schema changes. Rockset just sucks in all the raw data and makes it accessible using SQL, so it's faster and easier to develop on the data.”
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Data testing tools: Key capabilities you should know
Databand.ai
AUGUST 30, 2023
Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.
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How to Become a Big Data Engineer in 2023
ProjectPro
SEPTEMBER 26, 2021
A Master’s degree in Computer Science, Information Technology, Statistics, or a similar field is preferred with 2-5 years of experience in Software Engineering/Data Management/Database handling is preferred at an intermediate level. For example, Apache has developed numerous Big Data tools, such as Hadoop, Spark, and others.
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Machine Learning Engineer vs Data Scientist - The Differences
ProjectPro
DECEMBER 16, 2021
If you look at the machine learning project lifecycle , the initial data preparation is done by a Data Scientist and becomes the input for machine learning engineers. Later in the lifecycle of a machine learning project, it may come back to the Data Scientist to troubleshoot or suggest some improvements if needed.
What Is Sales Operations? Process, Roles, Responsibilities
U-Next
SEPTEMBER 23, 2022
At first, the sales team operation consisted of a small team of experts who analyzed financial data, prepared reports, and forecasted sales for the company. As part of their role, they are responsible for managing the objectives in the sales technology stack to enable the sales reps to focus on selling. . Strategy .
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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly
ProjectPro
FEBRUARY 8, 2023
AWS Glue Use Cases Whether it is integrating data from multiple sources or migrating data from on-premises to cloud or preparing data for training machine learning models - AWS Glue can be leveraged for a variety of use cases in your next big data project. You can use Glue's G.1X
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The Good and the Bad of Databricks Lakehouse Platform
AltexSoft
MARCH 30, 2023
What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.
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