The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

The Data Lake: A Reservoir of Unstructured Potential A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs.

Data Management 52

Data Management Management Data Lake Data Governance 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. 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. Structured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse?

Engineering 84

Engineering Raw Data Data Science Machine Learning 84

Edureka

AUGUST 2, 2023

Organisations and businesses are flooded with enormous amounts of data in the digital era. Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation.

Data Process 52

Data Process Process Data Cleanse Data Mining 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. 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?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Grouparoo

JANUARY 11, 2022

The integration of data from separate sources becomes a self-consistent data set with the removal of duplications and flagging of inconsistencies or, if possible, their resolution. Data storage uses a non-volatile environment with strict management controls on the modification and deletion of data.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more. Find sources of relevant data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Edureka

JULY 4, 2024

Amazon EMR owns and maintains the heavy-lifting hardware that your analyses require, including data storage, EC2 compute instances for big jobs and process sizing, and virtual clusters of computing power. Let’s see what is AWS EMR, its features, benefits, and especially how it helps you unlock the power of your big data.

AWS 52

AWS Amazon Web Services Hadoop Big Data 52

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

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 Relational Database AWS 52

AltexSoft

NOVEMBER 10, 2021

The data in this case is checked against the pre-defined schema (internal database format) when being uploaded, which is known as the schema-on-write approach. Purpose-built, data warehouses allow for making complex queries on structured data via SQL (Structured Query Language) and getting results fast for business intelligence.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Knowledge Hut

JANUARY 18, 2024

4 Purpose Utilize the derived findings and insights to make informed decisions The purpose of AI is to provide software capable enough to reason on the input provided and explain the output 5 Types of Data Different types of data can be used as input for the Data Science lifecycle.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Edureka

JANUARY 18, 2024

Introduction of R as an optional language in data science, highlighting its strengths in statistics and visualization. Data Manipulation Examine the most important data manipulation libraries like explore Pandas for structured data manipulation and Numpy for numerical operations in Python.

Data Science 40

Data Science Deep Learning Machine Learning NoSQL 40

ProjectPro

AUGUST 11, 2021

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Edureka

JULY 5, 2024

Tableau Prep has brought in a new perspective where novice IT users and power users who are not backward faithfully can use drag and drop interfaces, visual data preparation workflows, etc., simultaneously making raw data efficient to form insights. BigQuery), or another data storage solution.

Data Preparation 40

Data Preparation Process BI ETL Tools 40

Edureka

JUNE 1, 2023

Data Analytics tools and technologies offer opportunities and challenges for analyzing data efficiently so you can better understand customer preferences, gain a competitive advantage in the marketplace, and grow your business. What is Data Analytics? Data analytics is the process of converting raw data into actionable insights.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

Knowledge Hut

MARCH 27, 2024

The collection of meaningful market data has become a critical component of maintaining consistency in businesses today. A company can make the right decision by organizing a massive amount of raw data with the right data analytic tool and a professional data analyst.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. And most of this data has to be handled in real-time or near real-time.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 31, 2022

Provides Powerful Computing Resources for Data Processing Before inputting data into advanced machine learning models and deep learning tools, data scientists require sufficient computing resources to analyze and prepare it. Unlock the ProjectPro Learning Experience for FREE How Does Snowflake Store Data Internally?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

AltexSoft

SEPTEMBER 10, 2021

With it, data is retrieved from its sources, migrated to a staging data repository where it undergoes cleaning and conversion to be further loaded into a target source (commonly data warehouses or data marts ). A newer way to integrate data into a centralized location is ELT. How data consolidation works.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

SEPTEMBER 23, 2021

The result of experimentation supplies downstream applications with prepared data. A data hub serves as a gateway to dispense the required data. So the use of unstructured or semi-structured data is also available in a data hub, since a data lake can be a part of it. Azure Data Factory.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Monte Carlo

AUGUST 31, 2023

Data pipelines can handle both batch and streaming data, and at a high-level, the methods for measuring data quality for either type of asset are much the same. We’ll take a closer look at variables that can impact your data next. Rise of the Data Lakehouse Data warehouse or data lake?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

ProjectPro

AUGUST 18, 2021

By the end of 2022, the industry will experience a huge demand for data analysts, data scientists, and BI professionals with decent Tableau knowledge. . · · Tableau also provides a data blending facility. Which Tableau data types are preferable while dealing with structured data?

BI 40

BI SQL Database-centric Software Engineering 40

ProjectPro

JULY 27, 2021

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis , data migration, data processing architecture, data storage, big data analytics, etc. Structured data usually consists of only text.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

ProjectPro

NOVEMBER 14, 2015

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis.

Hadoop 52

Hadoop Project Big Data Healthcare 52

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Knowledge Hut

NOVEMBER 28, 2023

Here Data Science becomes relevant as it deals with converting unstructured and messy data into structured data sets for actionable business insights. Data Science is also concerned with analyzing, exploring, and visualizing data, thereby assisting the company's growth. Who is a Data Architect?

Data Architect 52

Data Architect Data Science Certification Big Data 52

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

Knowledge Hut

DECEMBER 26, 2023

Businesses benefit at large with these data collection and analysis as they allow organizations to make predictions and give insights about products so that they can make informed decisions, backed by inferences from existing data, which, in turn, helps in huge profit returns to such businesses. What is the role of a Data Engineer?

Data Engineer 98

Data Engineer Data Engineering Engineering Pipeline-centric 98

ProjectPro

MAY 31, 2021

To build a big data project, you should always adhere to a clearly defined workflow. 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.

Big Data 52

Big Data Coding Project Hadoop 52

AltexSoft

MARCH 14, 2023

Batch jobs are often scheduled to load data into the warehouse, while real-time data processing can be achieved using solutions like Apache Kafka and Snowpipe by Snowflake to stream data directly into the cloud warehouse. But this distinction has been blurred with the era of cloud data warehouses.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

MARCH 14, 2014

14 Hulu Video Delivery 13 machine clusters – 8 cores, 4 TB Used for analysis and log storage 15 Last.fm Online FM Music 100 nodes, 8 TB storage Calculation of charts and data testing 16 IMVU Social Games Clusters up to 4 m1.large Hadoop is used at eBay for Search Optimization and Research.

Hadoop 40

Hadoop Big Data Data Mining Retail 40