Knowledge Hut

JANUARY 18, 2024

For more information, check out the best Data Science certification. A data scientist’s job description focuses on the following – Automating the collection process and identifying the valuable data. Roles and Responsibilities Write data collection and processing procedures.

Data Science 98

Data Science Business Analyst ETL Method Data Architect 98

Precisely

SEPTEMBER 6, 2023

To truly succeed in an increasingly data-driven world, organizations need data governance. Data governance is the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset. The same does not hold true for unstructured data.

Data Governance 52

Data Governance Big Data Government Unstructured Data 52

AltexSoft

NOVEMBER 23, 2021

The traditional way of data integration involves consolidating disparate data within a single repository — commonly a data warehouse — via the extract, transform, load (ETL) process. You can learn more about how such data pipelines are built in our video about data engineering. Data virtualization use cases.

Process 69

Process Data Lake Metadata Data Warehouse 69

Snowflake

JANUARY 18, 2024

A robust, flexible architecture Snowflake’s unique architecture is designed to handle the full volume, velocity and variety of data without making manufacturers deal with downtime for upgrades or compute changes. In addition, they can add third-party data sets through Snowflake Marketplace to enrich insights.

Manufacturing 87

Manufacturing Unstructured Data Cloud Architecture 87

ProjectPro

NOVEMBER 30, 2021

Now let us try to understand ETL data pipelines in more detail. It was a manual process and very challenging as well. ETL Pipeline vs. Data Pipeline - The Differences It is common to interchangeably refer to ETL (Extract, Transform, and Load) pipelines and data pipelines.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Knowledge Hut

APRIL 23, 2024

These data sets consist of extensive and intricate data from diverse sources, including business transactions, social media interactions, and sensor data. Big data stands out due to its significant volume, quick velocity, and wide variety, leading to difficulties in storage, processing, analysis, and interpretation.

Big Data 52

Big Data Media Datasets Unstructured Data 52

DoorDash Engineering

APRIL 23, 2024

To ensure our catalog’s quality does not degrade, we standardize and enrich raw merchant data. Historically, this SKU enrichment of extracting and tagging attributes has been a purely manual process led by contract operators. As our catalog expands, we seek new approaches driven by machine learning to auto-enrich SKU data.

Building 91

Building Retail Manufacturing Unstructured Data 91

Knowledge Hut

DECEMBER 26, 2023

Data Science is the study of extracting insights from massive amounts of data using various scientific approaches, processes and algorithms. The development of big data, data analysis, and quantitative statistics has given rise to the term "data science." Data science is now more important than ever.

Data Science 52

Data Science Unstructured Data Medical Healthcare 52

Knowledge Hut

APRIL 23, 2024

Business Intelligence (BI) refers to the use of technology and processes to gather, analyze, and present business data in a way that helps organizations make informed decisions. NLP also allows businesses to generate insights from unstructured data sources like customer feedback and social media.

Business Intelligence 52

Business Intelligence BI Data Analysis Media 52

Knowledge Hut

JANUARY 18, 2024

Data Science is a field of study that handles large volumes of data using technological and modern techniques. This field uses several scientific procedures to understand structured, semi-structured, and unstructured data. Both data science and software engineering rely largely on programming skills.

Software Engineer 97

Software Engineer Software Engineering Data Science Engineering 97

U-Next

MARCH 1, 2023

Being a hybrid role, Data Engineer requires technical as well as business skills. They build scalable data processing pipelines and provide analytical insights to business users. A Data Engineer also designs, builds, integrates, and manages large-scale data processing systems. What is a data warehouse?

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Knowledge Hut

JULY 28, 2023

Data Types and Dimensionality ML algorithms work well with structured and tabular data, where the number of features is relatively small. DL models excel at handling unstructured data such as images, audio, and text, where the data has a large number of features or high dimensionality. What is Machine Learning?

Deep Learning 52

Deep Learning Machine Learning Unstructured Data Algorithm 52

Towards Data Science

DECEMBER 4, 2023

We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Lake A data lake would serve as a repository for raw and unstructured data generated from various sources within the Formula 1 ecosystem: telemetry data from the cars (e.g.

Data Engineering 83

Data Engineering Data Engineer Engineering Data Lake 83

Cloudera

FEBRUARY 8, 2021

The goal is to define, implement and offer a data lifecycle platform enabling and optimizing future connected and autonomous vehicle systems that would train connected vehicle AI/ML models faster with higher accuracy and delivering a lower cost.

Manufacturing 108

Manufacturing Machine Learning Data Ingestion Electronics 108

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. These data have been accessible to us because of the advanced and latest technologies which are used in the collection of data.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Knowledge Hut

FEBRUARY 28, 2024

In this blog, we will explore the future of big data in business, its applications, and the technologies that will drive its evolution. What is Big Data? Big data refers to large amounts of data. The differentiation between data and big data becomes clear once we look at the methods of analyzing them.

Big Data 52

Big Data Cloud Storage Healthcare SQL 52

Knowledge Hut

JUNE 20, 2023

Additionally, they create and test the systems necessary to gather and process data for predictive modelling. Data engineers play three important roles: Generalist: With a key focus, data engineers often serve in small teams to complete end-to-end data collection, intake, and processing.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Knowledge Hut

JULY 26, 2023

Veracity meaning in big data is the degree of accuracy and trustworthiness of data, which plays a pivotal role in deriving meaningful insights and making informed decisions. This blog will delve into the importance of veracity in Big Data, exploring why accuracy matters and how it impacts decision-making processes.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Grouparoo

JANUARY 11, 2022

When it comes to storing large volumes of data, a simple database will be impractical due to the processing and throughput inefficiencies that emerge when managing and accessing big data. This article looks at the options available for storing and processing big data, which is too large for conventional databases to handle.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

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. How ELT Works The process of ELT can be broken down into the following three stages: 1.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Knowledge Hut

JANUARY 30, 2024

Whether you're a seasoned data scientist or just stepping into the world of data, come with me as we unravel the secrets of data extraction and learn how it empowers us to unleash the full potential of data. What is data extraction? Use Case Essential for data preprocessing and creating usable datasets.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Knowledge Hut

JUNE 23, 2023

Data engineers make a tangible difference with their presence in top-notch industries, especially in assisting data scientists in machine learning and deep learning. You should have the expertise to collect data, conduct research, create models, and identify patterns. What’s the Demand for Data Engineers?

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

ProjectPro

JUNE 18, 2015

Big Data holds the promise of changing how businesses and people solve real world problems and Crowdsourcing plays a vital role in managing big data. Let’s understand how crowdsourcing big data can revolutionize business processes. When we think of big data, we think of enterprise crowdsourcing.

Big Data 52

Big Data Process Data Cleanse Data Analytics 52

Cloudera

AUGUST 20, 2021

It is both the superior technical characteristics of each individual data experience and the cohesive choreography between them that make CDP the ideal data platform for complex data products that include multiple stages of analytical processing to deliver differentiated value propositions. Build Once, Scale Anywhere.

Data Warehouse 96

Data Warehouse Data Architecture Cloud 96

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

FEBRUARY 21, 2023

A data fabric isn’t a standalone technology—it’s a data management architecture that leverages an integrated data layer atop underlying data in order to empower business leaders with real-time analytics and data-driven insights. A data fabric offers unity in a formerly disconnected, incompatible data environment.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

A data fabric isn’t a standalone technology—it’s a data management architecture that leverages an integrated data layer atop underlying data in order to empower business leaders with real-time analytics and data-driven insights. A data fabric offers unity in a formerly disconnected, incompatible data environment.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

ProjectPro

JANUARY 27, 2023

It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange.

BI 52

BI ETL Tools Retail Healthcare 52

Knowledge Hut

MAY 9, 2024

In this blog post, we will look at some of the world's highest paying data science jobs, what they entail, and what skills and experience you need to land them. What is Data Science? Data science also blends expertise from various application domains, such as natural sciences, information technology, and medicine.

Data Science 52

Data Science Data Mining Data Architect Programming Language 52

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Knowledge Hut

DECEMBER 26, 2023

You can execute this by learning data science with python and working on real projects. These skills are essential to collect, clean, analyze, process and manage large amounts of data to find trends and patterns in the dataset. The dataset can be either structured or unstructured or both.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Knowledge Hut

MAY 31, 2023

Artificial intelligence (AI) projects are software-based initiatives that utilize machine learning, deep learning, natural language processing, computer vision, and other AI technologies to develop intelligent programs capable of performing various tasks with minimal human intervention.

Project 96

Project Healthcare Deep Learning Transportation 96

DareData

JULY 5, 2023

In today's data-driven world, organizations are faced with the challenge of managing and processing large volumes of data efficiently. To overcome this challenge, many companies are turning to Data Lake solutions, which provide a centralized and scalable platform for storing, processing, and analyzing data.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. As a result, alternative data integration technologies (e.g.,

Government 92

Government Hadoop Data Security Data Warehouse 92

Knowledge Hut

MARCH 27, 2024

Big data tools are used to perform predictive modeling, statistical algorithms and even what-if analyses. Some important big data processing platforms are: Microsoft Azure. Why Is Big Data Analytics Important? Some open-source technology for big data analytics are : Hadoop. Apache Spark. Apache Storm. Apache SAMOA.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52