The Modern Data Company

APRIL 15, 2024

It offers high-quality, well-managed data streams from every corner of your company, offering clear insights to your tech teams and, critically, your business leaders—your CMOs, CFOs, and CSOs. A Closer Look The biggest draw of data products for business stakeholders is that they enable Transparent Metric Trees.

Raw Data 52

Raw Data Government Data Datasets 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

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

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.

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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?

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ETL Tools Database-centric Data Mining Data Cleanse 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

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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Data Cleanse Data Pipeline Datasets Data Validation 52

Ascend.io

NOVEMBER 21, 2023

More importantly, we will contextualize ELT in the current scenario, where data is perpetually in motion, and the boundaries of innovation are constantly being redrawn. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

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Raw Data Data Warehouse Data Cleanse Data Integration 52

Striim

NOVEMBER 17, 2023

Prerequisites Before we embark on the journey of integrating Striim with Google BigQuery ML for real-time data processing in machine learning, there are a few prerequisites that you should ensure are in place. Striim Instance: To get started, you need to have a Striim instance created and have access to it. MODEL ancient-yeti-175123.DMS_SAMPLE.striim_bq_model,

Machine Learning 52

Machine Learning Data Process PostgreSQL Process 52

Knowledge Hut

NOVEMBER 27, 2023

A data scientist’s job needs loads of exploratory data research and analysis on a daily basis with the help of various tools like Python, SQL, R, and Matlab. This role is an amalgamation of art and science that requires a good amount of prototyping, programming and mocking up of data to obtain novel outcomes.

Data Science 52

Data Science Datasets Machine Learning Portfolio 52

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.

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Machine Learning Medical Algorithm Deep Learning 98

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.

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Data Lake Building Raw Data ETL Tools 52

Knowledge Hut

JULY 28, 2023

DL models automatically learn features from raw data, eliminating the need for explicit feature engineering. Data Requirements ML models typically require more labelled training data to achieve good performance. Machine Learning vs Deep Learning: Used for Let us now see when to use deep learning vs machine learning.

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Deep Learning Machine Learning Unstructured Data Algorithm 52

Knowledge Hut

DECEMBER 7, 2023

Given the rising importance of data with each passing day, I believe I will continue doing so in the coming years. Introducing Microsoft Power BI , a leading solution in this domain, which enables users to transform raw data into insightful visualizations and reports. Power BI allows you access to so many opportunities.

BI 52

BI Certification Business Intelligence Raw Data 52

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Raw data store section. Transformation section.

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Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

JANUARY 24, 2024

This blog offers an exclusive glimpse into the daily rituals, challenges, and moments of triumph that punctuate the professional journey of a data scientist. The primary objective of a data scientist is to analyze complex datasets to uncover patterns, trends, and valuable information that can aid in informed decision-making.

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Database-centric Data Science Machine Learning Datasets 52

Knowledge Hut

JANUARY 22, 2024

Check out the Data Science course fee to start your journey. Why is Data Science So Important? Data is not useful until it is transformed into valuable information. Mining large datasets containing structured and unstructured data and identifying hidden patterns to gain actionable insights are two main tasks in data science.

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Data Science Banking Big Data Insurance 52

DataKitchen

MAY 4, 2023

Data Mesh Bergh explained that the Data Mesh organizes a team’s work into chunks called decentralized domains. Instead of boiling the ocean and focusing on all datasets and customers, the Data Mesh focuses on fewer datasets and customers, which reduces complexity and helps get more done.

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Raw Data Data Datasets Metadata 52

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.

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Datasets Entertainment Data Workflow Data 77

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. With strong G2 scores (4.7

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Knowledge Hut

JUNE 28, 2023

By examining these factors, organizations can make informed decisions on which approach best suits their data analysis and decision-making needs. Parameter Data Mining Business Intelligence (BI) Definition The process of uncovering patterns, relationships, and insights from extensive datasets.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

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. Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content.

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Data Cleanse Data Validation Data Pipeline Datasets 52

Knowledge Hut

SEPTEMBER 27, 2023

The former empowers the data visualizations and analytical metrics from the raw data so businesses can use them to get insights and take action. Salesforce holds the capability to integrate with various data sources such as internal (customer records, interactions, leads, etc.),

BI 52

BI Datasets Data Security Data Analysis 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.

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BI Business Intelligence Machine Learning SQL 98

Knowledge Hut

NOVEMBER 20, 2023

Here's how it works: Data Collection: The first step is to gather data. This data may contain transaction histories, client information, and past fraud incidents in the context of fraud detection. Data Preprocessing: Raw data often contains noise and irrelevant information.

Insurance 52

Insurance Banking Machine Learning Algorithm 52

RandomTrees

DECEMBER 20, 2023

In this way, Edge AI facilitates the storage and computation of data, making it more accessible for users. Moreover, Edge AI combines AI algorithms with edge computing capabilities on local devices. Thus, data can get processed and analyzed without requiring connectivity and integration, allowing users to access data from various sources.

IT 52

IT Hospitality Healthcare Deep Learning 52

Cloud Academy

JANUARY 27, 2022

A data engineer is an engineer who creates solutions from raw data. A data engineer develops, constructs, tests, and maintains data architectures. Let’s review some of the big picture concepts as well finer details about being a data engineer. Earlier we mentioned ETL or extract, transform, load.

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Certification Data Engineering Data Engineer Engineering 52

Ascend.io

SEPTEMBER 14, 2023

We’ll explore its advantages, delve into its applications, and highlight why Python is increasingly becoming the first choice for data engineers worldwide. Why Python for Data Engineering? As the field of data engineering evolves, the need for a versatile, performant, and easily accessible language becomes paramount.

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Data Engineering Data Engineer Python Engineering 52

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.

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Data Pipeline Raw Data Data Schemas Healthcare 93

ProjectPro

JANUARY 19, 2022

The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis. That needs to be done because raw data is painful to read and work with. Independently create data-driven solutions that are accurate and informative.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

OCTOBER 4, 2023

While the numbers are impressive (and a little intimidating), what would we do with the raw data without context? The tool will sort and aggregate these raw data and transport them into actionable, intelligent insights. csv) – They are simplified text fields with rows of data. Comma-separated values (.csv)

BI 52

BI Systems Raw Data Business Intelligence 52

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 109

Deep Learning Machine Learning Raw Data Data Ingestion 109

ProjectPro

DECEMBER 7, 2021

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. It can also be made accessible as an API and distributed to stakeholders.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Edureka

AUGUST 2, 2023

Python offers a strong ecosystem for data scientists to carry out activities like data cleansing, exploration, visualization, and modeling thanks to modules like NumPy, Pandas, and Matplotlib. Data scientists can also organize unstructured raw data using SQL so that it can be analyzed with statistical and machine learning methods.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

Precisely

APRIL 24, 2023

Improving data quality and enriching internal datasets with curated data from trusted sources sound good. To sell your initiative to executive sponsors, deliver results, earn ongoing support, and connect the dots between data analytics and your organization’s strategic priorities. Nice to have” simply isn’t good enough.

Retail 52

Retail Raw Data High Quality Data Banking 52

Knowledge Hut

SEPTEMBER 27, 2023

SPICE, an in-memory computation engine, is used to ensure rapid data analysis. SPICE is capable of handling large datasets, allowing for real-time analytics and interactive dashboards Power BI's DAX (Data Analysis Expressions) language prioritizes performance.

BI 52

BI AWS Database-centric Data Lake 52

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.

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