Christophe Blefari

APRIL 21, 2023

Access — you will be able to namespace models with groups and visibility. Data Engineering at Adyen — "Data engineers at Adyen are responsible for creating high-quality, scalable, reusable and insightful datasets out of large volumes of raw data" This is a good definition of one of the possible responsibilities of DE.

Raw Data 130

Raw Data Data SQL Datasets 130

Precisely

NOVEMBER 20, 2023

Imagine accessing more detail based on each customer’s home address. You need reference data sets from trusted, authoritative sources like Precisely to do that. Enrichment: The Secret to Supercharged AI You’re not just improving accuracy by augmenting your datasets with additional information.

Raw Data 121

Raw Data Insurance Data Portfolio 121

Towards Data Science

FEBRUARY 6, 2024

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

Systems 76

Systems Raw Data Metadata Data Cleanse 76

Precisely

DECEMBER 13, 2023

Digital marketing is ideally suited for precise targeting and rapid feedback, provided that business users have access to the detailed demographic and geospatial data they need. These datasets offer information for over 140 countries and territories worldwide, and empower businesses to conduct their analysis at scale.

Raw Data 96

Raw Data Entertainment Data Validation Education 96

Cloudera

NOVEMBER 17, 2020

Once the prototype has been completely deployed, you will have an application that is able to make predictions to classify transactions as fraudulent or not: The data for this is the widely used credit card fraud dataset. Data analysis – create a plan to build the model.

Deep Learning 116

Deep Learning Machine Learning Raw Data Data Ingestion 116

AltexSoft

AUGUST 25, 2021

You can’t simply feed the system your whole dataset of emails and expect it to understand what you want from it. It’s called deep because it comprises many interconnected layers — the input layers (or synapses to continue with biological analogies) receive data and send it to hidden layers that perform hefty mathematical computations.

Process 139

Process Deep Learning Datasets Machine Learning 139

LinkedIn Engineering

APRIL 4, 2023

Figure 3: An expanded metric detail view showing historical trend and dimensional breakdown (note all data is mocked) Finally, we link to a dedicated dashboard for each metric with further slicing and dicing capabilities, and access to the individual data points. We did that by separating the metric’s configuration logic (i.e.

MySQL 122

MySQL Datasets Software Engineer Software Engineering 122

U-Next

MARCH 7, 2023

Most Data Analysts do not require a deep understanding of complex mathematics, even though they should have a foundational knowledge of statistics and mathematics. Statistics, linear algebra, and calculus are generally required for Data Analysts. Why is MS Access important in Data Analytics? What is data extraction?

Data Mining 96

Data Mining Government Scala Data Governance 96

Knowledge Hut

JULY 4, 2023

Data analysis and Interpretation: It helps in analyzing large and complex datasets by extracting meaningful patterns and structures. By identifying and understanding patterns within the data, valuable insights can be gained, leading to better decision-making, and understanding of underlying relationships.

Machine Learning 98

Machine Learning Medical Algorithm Deep Learning 98

Confluent

SEPTEMBER 26, 2019

® , Go, and Python SDKs where an application can use SQL to query raw data coming from Kafka through an API (but that is a topic for another blog). Let’s now dig a little bit deeper into Kafka and Rockset for a concrete example of how to enable real-time interactive queries on large datasets, starting with Kafka.

Kafka 21

Kafka SQL BI Hadoop 21

Knowledge Hut

DECEMBER 22, 2023

Linear Algebra Linear Algebra is a mathematical subject that is very useful in data science and machine learning. A dataset is frequently represented as a matrix. Statistics Statistics are at the heart of complex machine learning algorithms in data science, identifying and converting data patterns into actionable evidence.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Striim

SEPTEMBER 11, 2024

Understanding the essential components of data pipelines is crucial for designing efficient and effective data architectures. Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses.

AWS 98

AWS Scala Metadata Data Lake 98

Precisely

SEPTEMBER 19, 2024

It’s important to note that in the AI age, your data governance practices must evolve ensure the ethical use, privacy, and compliance of these technologies. Let’s examine these measures more closely: Data privacy: Implement strict access control policies and mask personally identifiable information (PII) data to protect sensitive information.

Data Governance 78

Data Governance Government High Quality Data Datasets 78

Towards Data Science

NOVEMBER 16, 2023

Unlike streaming data, which needs instant processing, batch data can be processed at scheduled intervals or when resources become available. Splittable in chunks : instead of processing an entire dataset in a single, resource-intensive operation, batch data can be divided into smaller, more manageable segments.

Data Process 72

Data Process Process Raw Data Data 72

Ascend.io

OCTOBER 28, 2024

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Towards Data Science

MARCH 9, 2023

Code implementations for ML pipelines: from raw data to predictions Photo by Rodion Kutsaiev on Unsplash Real-life machine learning involves a series of tasks to prepare the data before the magic predictions take place. Those are the features and their respective data types: Image 1 —Features and data types.

Machine Learning 72

Machine Learning Building Datasets Big Data 72

Knowledge Hut

DECEMBER 7, 2023

Welcome to the comprehensive guide for beginners on harnessing the power of Microsoft's remarkable data visualization tool - Power BI. In today's data-driven world, the ability to transform raw data into meaningful insights is paramount, and Power BI empowers users to achieve just that. What is Power BI?

BI 52

BI Raw Data Datasets Business Intelligence 52

Precisely

DECEMBER 5, 2024

Traditionally, data enrichment can be lengthy and expensive, but implementing best practices for evaluating datasets and working with a data provider that offers data delivery options fit for your needs can accelerate time to value while reducing costs. Ask yourself: Is the data format standardized? The top answer?

Datasets 69

Datasets Insurance Retail Data 69

Monte Carlo

JULY 25, 2023

Data curation is the process of transforming and enriching larger amounts of raw data into smaller, more widely accessible subsets of data that provide additional value to the organization or the intended use case. In This Article: What is data curation? Medallion architecture.

Raw Data 52

Raw Data Data Warehouse Data Architecture 52

Knowledge Hut

DECEMBER 22, 2023

Power BI is a technology-driven business intelligence tool or an array of software services, apps, and connectors to convert unrelated and raw data into visually immersive, coherent, actionable, and interactive insights and information. Microsoft developed it and combines business analytics, data visualization, and best practices.

BI 98

BI Business Intelligence Machine Learning SQL 98

Cloudera

DECEMBER 17, 2020

First and foremost, we designed the Cloudera Data Platform (CDP) to optimize every step of what’s required to go from raw data to AI use cases. In August 2020 we released CDP Data Engineering (DE) — our answer to enabling fast, optimized, and automated data engineering for analytic workloads.

Machine Learning 83

Machine Learning Data Science Data Pipeline Raw Data 83

Workfall

JULY 18, 2023

Behind the scenes, a team of data wizards tirelessly crunches mountains of data to make those recommendations sparkle. As one of those wizards, we’ve seen the challenges we face: the struggle to transform massive datasets into meaningful insights, all while keeping queries fast and our system scalable.

Datasets 77

Datasets Entertainment Data Workflow Data 77

Knowledge Hut

MAY 30, 2024

The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing. With good research and approach, analyzing this data can bring magical results.

Computer Science 98

Computer Science Data Mining Algorithm Machine Learning 98

Workfall

SEPTEMBER 18, 2023

Empowering Data-Driven Decisions: Whether you run a small online store or oversee a multinational corporation, the insights hidden in your data are priceless. Airbyte ensures that you don’t miss out on those insights due to tangled data integration processes.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

Databand.ai

JULY 6, 2023

7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More Joseph Arnold July 6, 2023 What Are Data Pipelines? Data pipelines are a series of data processing steps that enable the flow and transformation of raw data into valuable insights for businesses.

Data Pipeline 52

Data Pipeline Data Science Raw Data Media 52

dbt Developer Hub

SEPTEMBER 28, 2022

So let’s say that you have a business question, you have the raw data in your data warehouse , and you’ve got dbt up and running. You’re in the perfect position to get this curated dataset completed quickly! You’ve got three steps that stand between you and your finished curated dataset. Or are you?

Engineering 52

Engineering Raw Data Datasets BI 52

Knowledge Hut

JULY 28, 2023

Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. What is Data Labeling for Machine Learning?

Machine Learning 52

Machine Learning Process Datasets Raw Data 52

Knowledge Hut

JANUARY 29, 2024

In today's data-driven world, where information reigns supreme, businesses rely on data to guide their decisions and strategies. However, the sheer volume and complexity of raw data from various sources can often resemble a chaotic jigsaw puzzle.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Monte Carlo

JULY 30, 2024

Data teams can use uniqueness tests to measure their data uniqueness. Uniqueness tests enable data teams to programmatically identify duplicate records to clean and normalize raw data before entering the production warehouse.

Data Validation 52

Data Validation Datasets Medical Raw Data 52

Monte Carlo

OCTOBER 5, 2023

Enter the world of data clean rooms – the super secure havens where you can mix and mingle data from different sources to get insights without getting your hands dirty with the raw data. How data clean rooms work Data clean rooms combine and analyze different data sources without directly accessing the raw data.

Raw Data 52

Raw Data Medical Manufacturing AWS 52

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

Towards Data Science

MAY 14, 2024

Dataform enables the application of software engineering best practices such as testing, environments, version control, dependencies management, orchestration and automated documentation to data pipelines. This means dataset and tables generated from development workspace are manifested within the staging environment.

Data Pipeline 68

Data Pipeline Coding Raw Data Accessibility 68

Knowledge Hut

SEPTEMBER 26, 2023

The insights derived from the data in hand are then turned into impressive business intelligence visuals such as graphs or charts for the executive management to make strategic decisions. In this post, we will discuss the top power BI developer skills required to access Microsoft’s power business intelligence software.

BI 52

BI Business Intelligence Raw Data Data Analysis 52

Knowledge Hut

JANUARY 19, 2024

Embracing data science isn't just about understanding numbers; it's about wielding the power to make impactful decisions. Imagine having the ability to extract meaningful insights from diverse datasets, being the architect of informed strategies that drive business success. That's the promise of a career in data science.

Data Science 52

Data Science Machine Learning Datasets Algorithm 52

Towards Data Science

FEBRUARY 19, 2024

books, but there are millions in the Openlibrary data set , and I thought it was worthwhile to try adding more options to the search space. First we pull out the relevant columns from the raw data file. The important step here is turning the various functions that we need to apply to the data into Spark UDFs.

AWS 58

AWS Building Python Bytes 58

DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52