phData: Data Engineering

SEPTEMBER 19, 2023

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Knowledge Hut

APRIL 23, 2024

Big Data vs Small Data: Function Variety Big Data encompasses diverse data types, including structured, unstructured, and semi-structured data. It involves handling data from various sources such as text documents, images, videos, social media posts, and more.

Big Data 52

Big Data Datasets Data Analysis Media 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

Knowledge Hut

NOVEMBER 3, 2023

MongoDB is used for data science, meaning that we utilize the capabilities of this NoSQL database system as part of our data analysis and data modeling processes, which fall under the realm of data science. There are several benefits to MongoDB for data science operations. Why Use MongoDB for Data Science?

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

Towards Data Science

JANUARY 17, 2024

Parquet vs ORC vs Avro vs Delta Lake Photo by Viktor Talashuk on Unsplash The big data world is full of various storage systems, heavily influenced by different file formats. These are key in nearly all data pipelines, allowing for efficient data storage and easier querying and information extraction.

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Big Data Data Data Storage SQL 73

Knowledge Hut

JULY 26, 2023

Variety: Variety represents the diverse range of data types and formats encountered in Big Data. Traditional data sources typically involve structured data, such as databases and spreadsheets. Handling this variety of data requires flexible data storage and processing methods.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Silectis

JANUARY 3, 2021

Data Ingestion The process by which data is moved from one or more sources into a storage destination where it can be put into a data pipeline and transformed for later analysis or modeling. Data Integration Combining data from various, disparate sources into one unified view.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Rockset

JULY 29, 2022

Data warehouses are popular because they help break down data silos and ensure data consistency. You can aggregate and analyze relevant data from multiple sources without worrying about inconsistent and inaccessible data. Additionally, data warehouses are great at offering historical intelligence.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

AltexSoft

SEPTEMBER 10, 2021

To get a single unified view of all information, companies opt for data integration. In this article, you will learn what data integration is in general, key approaches and strategies to integrate siloed data, tools to consider, and more. What is data integration and why is it important?

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Data Integration Data Consolidation Data Warehouse BI 52

Knowledge Hut

MARCH 15, 2024

RDBMS vs NoSQL: Features Here's a brief overview of the features of both RDBMS and NoSQL, pointwise: RDBMS: Utilizes structured tables to organize data. Enforces ACID (Atomicity, Consistency, Isolation, Durability) properties for data integrity. Queries are performed using SQL (Structured Query Language).

NoSQL 52

NoSQL Database-centric Relational Database PostgreSQL 52

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

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. Data Migration 2.

Hadoop 52

Hadoop Project Big Data Healthcare 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

The Modern Data Company

MAY 8, 2023

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 is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

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 is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

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 is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Data Lake Management Data Governance 52

Edureka

AUGUST 2, 2023

To make sure the data is precise and suitable for analysis, data processing analysts use methods including data cleansing, imputation, and normalisation. Data integration and transformation: Before analysis, data must frequently be translated into a standard format.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

Knowledge Hut

MARCH 27, 2024

is an all-in-one solution for businesses to connect their data and applications. It offers a cloud-based integration platform that helps businesses automate their data integration processes. Google Fusion Tables Google Fusion Tables is a web service that allows users to upload, merge, and visualize data tables.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

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

Knowledge Hut

DECEMBER 29, 2023

The highlight feature of this platform is its potential to integrate semi-structured and structured data without using any third-party tools. Apache Hive It is a Hadoop-based data management and storage tool that allows data analytics through an SQL-like framework.

Data Engineering 40

Data Engineering Data Engineer Engineering BI 40

AltexSoft

SEPTEMBER 23, 2021

A data hub is a central mediation point between various data sources and data consumers. It’s not a single technology, but rather an architectural approach that unites storages, data integration and orchestration tools. An ETL approach in the DW is considered slow, as it ships data in portions (batches.)

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

AUGUST 4, 2021

A data warehouse (DW) is a data repository that allows for storing and managing all the historical enterprise data, coming from disparate internal and external sources like CRMs, ERPs, flat files, etc. Initially, DWs dealt with structured data presented in tabular forms. Data mart implementation steps.

Data Lake 64

Data Lake Data Warehouse ETL Tools Telecommunication 64

AltexSoft

MARCH 2, 2023

Data usability ensures that data is available in a structured format that is compatible with traditional business tools and software. Data integrity is about maintaining the quality of data as it is stored, converted, transmitted, and displayed. Learn more about data integrity in our dedicated article.

Data Governance 52

Data Governance Government Healthcare Programming 52

Edureka

FEBRUARY 7, 2023

The main advantage of Azure Files over Azure Blobs is that it allows for folder-based data organisation and is SMB compliant, allowing for use as a file share. Storage for Azure VMs is provided by Azure Disks (Virtual Machines). 13) Examine the capabilities of Azure storage explorer. 15) What is Azure table storage, exactly?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

ProjectPro

JANUARY 31, 2022

Snowflake puts all data on a single high-performance platform by bringing data in from many locations, reducing the complexity and delay imposed by standard ETL processes. Snowflake allows data to be examined and cleaned immediately, assuring data integrity. Data security, as data is not accessible by humans.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 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

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

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Calcite has chosen to stay out of the data storage and processing business.

Big Data 52

Big Data Project Metadata Programming Language 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

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.

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Unstructured Data NoSQL Hadoop Data Lake 59

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

ProjectPro

MAY 31, 2021

For such scenarios, data-driven integration becomes less comfortable, so you must prefer event-based data integration. This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow.

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

JUNE 4, 2015

Defining Architecture Components of the Big Data Ecosystem Core Hadoop Components 3) MapReduce- Distributed Data Processing Framework of Apache Hadoop MapReduce Use Case: >4)YARN Key Benefits of Hadoop 2.0 2) Hadoop Distributed File System (HDFS) - The default big data storage layer for Apache Hadoop is HDFS.

Hadoop 52

Hadoop Architecture IT Java 52

Knowledge Hut

MARCH 7, 2024

Data analytics tools in big data includes a variety of tools that can be used to enhance the data analysis process. These tools include data analysis, data purification, data mining, data visualization, data integration, data storage, and management.

Big Data 52

Big Data Data Analytics Data Mining MongoDB 52