Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional responsible for designing, implementing, and managing data solutions using Microsoft's Azure cloud platform. They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

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

JANUARY 20, 2021

We have simplified this journey into five discrete steps with a common sixth step speaking to data security and governance. The six steps are: Data Collection – data ingestion and monitoring at the edge (whether the edge be industrial sensors or people in a brick and mortar retail store). Data Collection Challenge.

Manufacturing 107

Manufacturing Data Warehouse Kafka Retail 107

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. Unstructured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Data Lake Management Data Governance 52

Towards Data Science

MAY 3, 2023

One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. VDK ingests data from the Data Source.

Data Lake 84

Data Lake Data SQL Data Warehouse 84

Snowflake

MARCH 27, 2023

It seems everyone has a handful of such shapes in their raw data, and in the past they had to fix those shapes outside of Snowflake before ingesting them. Introducing CARTO Workflows Snowflake’s powerful data ingestion and transformation features help many data engineers and analysts who prefer SQL.

Generalist 52

Generalist Raw Data Business Analyst SQL 52

AltexSoft

MAY 12, 2023

You may encounter unstructured data in the form of text documents, which can be plain text files (.txt), html), and other word processing formats. As a form of electronic communication, emails often contain unstructured text data and various file attachments, such as images, documents, or spreadsheets. doc,docx), PDF files (.pdf),

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Ascend.io

JULY 28, 2023

Data pipeline architecture is a framework that outlines the flow and management of data from its original source to its final destination within a system. This framework encompasses the steps of data ingestion, transformation, orchestration, and sharing.

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

DECEMBER 7, 2021

Data Pipeline Tools AWS Data Pipeline Azure Data Pipeline Airflow Data Pipeline Learn to Create a Data Pipeline FAQs on Data Pipeline What is a Data Pipeline? A pipeline may include filtering, normalizing, and data consolidation to provide desired data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

APRIL 25, 2024

There is much to learn and return to general data science, especially as the world is ever-changing. Modern data science has already developed refined techniques that have benefited industries worldwide. Big data processing is aided by using computer languages like Python, SQL, and R.

Data Science 52

Data Science Telecommunication Transportation Big Data 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Be sure to read to the end where we share 6 data pipeline architecture diagrams used by real data teams at companies like JetBlue, Fox Networks, Drata, and more. What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Monte Carlo

AUGUST 19, 2023

A data platform, often referred to as a ‘modern data stack,’ is the central processing hub for an organization’s data ecosystem. We’ll cover: What is a data platform? A data platform is a central repository and processing house for all of an organization’s data.

Building 52

Building BI Data Lake Data Governance 52

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

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.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

AltexSoft

MARCH 30, 2023

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. The relatively new storage architecture powering Databricks is called a data lakehouse. Who makes data useful?

Scala 64

Scala Data Lake BI Google Cloud 64

Snowflake

SEPTEMBER 11, 2023

When the SQL query utilizing that stream is used to push the data and transform data into a target table ( DML queries), each offset on each underlying table is independent and processes new change-tracked records. Snowflake will find that record in the other table to support your SQL join and process that record.

SQL 71

SQL Data Raw Data Architecture 71

ProjectPro

JANUARY 31, 2023

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

FEBRUARY 25, 2022

A machine learning pipeline helps automate machine learning workflows by processing and integrating data sets into a model, which can then be evaluated and delivered. Increased Adaptability and Scope Although you require different models for different purposes, you can use the same functions/processes to build those models.

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

U-Next

JUNE 29, 2022

Layers of big data components compiled together to form a stack, and it isn’t as straightforward as collecting data and converting it into knowledge. . Data must be consumed from many sources, translated and stored, and then processed before being presented understandably. Extract Load and transform (ELT) .

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

U-Next

SEPTEMBER 23, 2022

To handle this large amount of data, we want a far more complicated architecture comprised of numerous components of the database performing various tasks rather than just one. . Real-life Examples of Big Data In Action . To address these issues, Big Data technologies such as Hadoop were established.

Big Data Ecosystem 40

Big Data Ecosystem Big Data Healthcare Data Lake 40

ProjectPro

AUGUST 24, 2021

Within no time, most of them are either data scientists already or have set a clear goal to become one. Nevertheless, that is not the only job in the data world. And, out of these professions, this blog will discuss the data engineering job role. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Zalando Engineering

MARCH 21, 2017

Instead, we can focus on building a flexible and versatile model that can be easily extended to new types of input data and applied to a variety of prediction tasks. In general, learning from raw data can help to avoid limitations when placing too much confidence in human domain modeling.

Deep Learning 52

Deep Learning Raw Data Machine Learning AWS 52

AltexSoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. What is Hadoop? But you can configure the block size.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

MAY 14, 2021

Not only does Big Data apply to the huge volumes of continuously growing data that come in different formats, but it also refers to the range of processes, tools, and approaches used to gain insights from that data. Key Big Data characteristics. Velocity is the speed at which the data is generated and processed.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Rockset

SEPTEMBER 6, 2019

This raw data from the devices needs to be enriched with content metadata and geolocation information before it can be processed and analyzed. From a data engineering perspective, this is a classic scenario of clickstream data, with a VR headset instead of a mouse.

Metadata 40

Metadata Kafka Data Cleanse SQL 40

ProjectPro

JULY 22, 2016

Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why. There are several people writing that data lakes are replacing data warehouses but this is just another technology hype that is coming across the effective use of data.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

MAY 31, 2021

Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation. Access Solution to Data Warehouse Design for an E-com Site 4.

Big Data 52

Big Data Coding Project Hadoop 52

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. Batch processing and reports after minutes or even hours is not sufficient. Apache Kafka is an event streaming platform that combines messages, storage, and data processing.

Kafka 20

Kafka BI SQL Datasets 20

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40