Striim

SEPTEMBER 11, 2024

The ingestion layer supports multiple data types and formats, including: Batch Data: Data collected and processed in discrete chunks, typically from static sources such as databases or logs. This method is advantageous when dealing with structured data that requires pre-processing before storage.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Monte Carlo

MARCH 3, 2025

Prior to data powering valuable data products like machine learning models and real-time marketing applications, data warehouses were mainly used to create charts in binders that sat off to the side of board meetings. The most common themes: Data readiness- You cant have good AI with bad data.

Unstructured Data 52

Unstructured Data Data Cloud Computing Banking 52

Knowledge Hut

FEBRUARY 29, 2024

Learning inferential statistics website: wallstreetmojo.com, kdnuggets.com Learning Hypothesis testing website: stattrek.com Start learning database design and SQL. A database is a structured data collection that is stored and accessed electronically. Considering this information database model is fitted with data.

Data Science 98

Data Science Datasets Machine Learning Database Design 98

Snowflake

JUNE 5, 2024

Cortex AI Cortex Analyst: Enable business users to chat with data and get text-to-answer insights using AI Cortex Analyst, built with Meta’s Llama 3 and Mistral Large models, lets you get the insights you need from your structured data by simply asking questions in natural language.

Coding 114

Coding Building Management Government 114

AltexSoft

JUNE 7, 2021

Master Nodes control and coordinate two key functions of Hadoop: data storage and parallel processing of data. Worker or Slave Nodes are the majority of nodes used to store data and run computations according to instructions from a master node. Data storage options. Data management and monitoring options.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Knowledge Hut

DECEMBER 26, 2023

According to the World Economic Forum, the amount of data generated per day will reach 463 exabytes (1 exabyte = 10 9 gigabytes) globally by the year 2025. The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

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

MAY 28, 2024

Ensuring all relevant data inputs are accounted for is crucial for a comprehensive ingestion process. In batch processing, this occurs at scheduled intervals, whereas real-time processing involves continuous loading, maintaining up-to-date data availability. Used for identifying and cataloging data sources.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

AltexSoft

MAY 12, 2023

Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. Unlike structured data, which is organized into neat rows and columns within a database, unstructured data is an unsorted and vast information collection.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Snowflake

MARCH 30, 2023

Snowflake can also ingest external tables from on-premise s data sources via S3-compliant data storage APIs. Batch/file-based data is modeled into the raw vault table structures as the hub, link, and satellite tables illustrated at the beginning of this post. Enter Snowpark !

Engineering 105

Engineering Raw Data Data Science Machine Learning 105

Cloudera

SEPTEMBER 15, 2022

Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

Systems 87

Systems Hadoop Metadata Telecommunication 87

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

Knowledge Hut

MARCH 15, 2024

Making decisions in the database space requires deciding between RDBMS (Relational Database Management System) and NoSQL, each of which has unique features. RDBMS uses SQL to organize data into structured tables, whereas NoSQL is more flexible and can handle a wider range of data types because of its dynamic schemas.

NoSQL 52

NoSQL Database-centric Relational Database PostgreSQL 52

phData: Data Engineering

SEPTEMBER 19, 2023

A data lake is a centralized repository containing extensive storage for raw, unfiltered data coming into a company’s data storage system. This data can be structured, semi-structured, or unstructured and comes from various sources such as databases, IoT devices, log files, etc.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Knowledge Hut

APRIL 23, 2024

Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data. Data warehousing offers several advantages. By structuring data in a predefined schema, data warehouses ensure data consistency and accuracy.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Knowledge Hut

APRIL 23, 2024

Big data and data mining are neighboring fields of study that analyze data and obtain actionable insights from expansive information sources. Big data encompasses a lot of unstructured and structured data originating from diverse sources such as social media and online transactions.

Data Mining 52

Data Mining Big Data Database-centric Unstructured Data 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 Management Data Lake 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 Management Data Lake 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 Management Data Lake Data Governance 52

Edureka

FEBRUARY 9, 2023

Data engineering is a new and evolving field that will withstand the test of time and computing advances. Certified Azure Data Engineers are frequently hired by businesses to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 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

Knowledge Hut

APRIL 25, 2023

Data engineering tools are software applications that help data engineers manage and process large and complex data sets. Data engineering is a field that requires a range of technical skills, including database management, data modeling, and programming.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 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 structure schemas.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

Knowledge Hut

SEPTEMBER 26, 2023

It offers a wide range of services, including computing, storage, databases, machine learning, and analytics, making it a versatile choice for businesses looking to harness the power of the cloud. This is particularly valuable in today's data landscape, where information comes in various shapes and sizes.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

Knowledge Hut

NOVEMBER 3, 2023

The need for efficient and agile data management products is higher than ever before, given the ongoing landscape of data science changes. MongoDB is a NoSQL database that’s been making rounds in the data science community. Let us see where MongoDB for Data Science can help you.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). The framework itself is extensible to run custom jobs.

Big Data 78

Big Data Data Management Management Metadata 78

ProjectPro

JANUARY 19, 2022

Data engineering is a new and ever-evolving field that can withstand the test of time and computing developments. Companies frequently hire certified Azure Data Engineers to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52

ProjectPro

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

ProjectPro

JANUARY 31, 2023

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data.

Big Data 52

Big Data Hadoop Relational Database AWS 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

ProjectPro

SEPTEMBER 9, 2021

The datasets are usually present in Hadoop Distributed File Systems and other databases integrated with the platform. Hive is built on top of Hadoop and provides the measures to read, write, and manage the data. Apache Hive Architecture Apache Hive has a simple architecture with a Hive interface, and it uses HDFS for data storage.

Hadoop 52

Hadoop Big Data Tools Java SQL 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

RandomTrees

SEPTEMBER 6, 2020

Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS). Built on new SQL database engine, it provides a unique architecture designed for the cloud.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineers are skilled professionals who lay the foundation of databases and architecture. Using database tools, they create a robust architecture and later implement the process to develop the database from zero. Data engineers who focus on databases work with data warehouses and develop different table schemas.

Data Engineering 98

Data Engineering Data Engineer Engineering Hadoop 98

AltexSoft

MARCH 14, 2023

These sources commonly include databases, SaaS products, and event streams. Databases store key information that powers a company’s product, such as user data and product data. The ones that keep only relational data in a tabular format are called SQL or relational database management systems (RDBMSs).

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

JANUARY 31, 2022

Snowflake provides data warehousing, processing, and analytical solutions that are significantly quicker, simpler to use, and more adaptable than traditional systems. Snowflake is not based on existing database systems or big data software platforms like Hadoop. Snowflake is a data warehousing platform that runs on the cloud.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Edureka

AUGUST 2, 2023

They are essential to the data lifecycle because they take unstructured data and turn it into something that can be used. They are responsible for processing, cleaning, and transforming raw data into a structured and usable format for further analysis or integration into databases or data systems.

Data Process 52

Data Process Process Data Cleanse Data Mining 52