AltexSoft

NOVEMBER 10, 2021

Well, there’s a new phenomenon in data management that received the name of a data lakehouse. The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. Data warehouse.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Monte Carlo

APRIL 1, 2021

Over the past few years, data lakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 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). While functional, our current setup for managing tables is fragmented.

Big Data 78

Big Data Data Management Management Metadata 78

AltexSoft

AUGUST 22, 2022

Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes. With these challenges in enterprise data management, there has to be an approach to overcoming them, right? The solution is called a data fabric.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

AltexSoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

Architecture 52

Architecture Data Lake Medical Datasets 52

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. Data integration with ETL has changed in the last three decades.

AWS 52

AWS Data Management ETL Tools Management 52

ProjectPro

JANUARY 31, 2022

This blog walks you through what does Snowflake do , the various features it offers, the Snowflake architecture, and so much more. Table of Contents Snowflake Overview and Architecture What is Snowflake Data Warehouse? Snowflake is not based on existing database systems or big data software platforms like Hadoop.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Towards Data Science

FEBRUARY 15, 2023

Data by itself has no value, it needs to be organized, standardized, and clean. In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture.

Data Lake 77

Data Lake Data Warehouse Hadoop Data Architecture 77

Data Engineering Podcast

AUGUST 31, 2020

Summary Data warehouse technology has been around for decades and has gone through several generational shifts in that time. The current trends in data warehousing are oriented around cloud native architectures that take advantage of dynamic scaling and the separation of compute and storage.

Data Warehouse 100

Data Warehouse Cloud Building Data Lake 100

ProjectPro

JULY 22, 2016

For the same cost, organizations can now store 50 times as much data as in a Hadoop data lake than in a data warehouse. Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Data Engineering Podcast

FEBRUARY 9, 2020

If you’re struggling with unwieldy dimensional models, slow moving projects, or challenges integrating new data sources then listen in on this conversation and then give data vault a try for yourself. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council.

Data Lake 100

Data Lake Data Warehouse Hadoop NoSQL 100

Cloudera

SEPTEMBER 12, 2022

First, organizations have a tough time getting their arms around their data. More data is generated in ever wider varieties and in ever more locations. Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making. Unified data fabric.

Unstructured Data 74

Unstructured Data Data Lake Data Architecture Data 74

Data Engineering Podcast

JUNE 17, 2021

Summary Working with unstructured data has typically been a motivation for a data lake. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable.

Unstructured Data 100

Unstructured Data Data Warehouse Metadata Media 100

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

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. 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.

Scala 64

Scala Data Lake BI Google Cloud 64

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

AltexSoft

MARCH 14, 2023

Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. A data stack, in turn, focuses on data : It helps businesses manage data and make the most out of it. Modern data stack architecture.

IT 59

IT Data Warehouse Data Governance Data Lake 59

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. What is a decentralized data architecture?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 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.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 83

Engineering Raw Data Data Science Scala 83

Knowledge Hut

DECEMBER 26, 2023

Data Engineering Data Engineering involves various operations like creating data pipelines, strategizing data management plans, processing data, and many more. Data Management Data management is an integral part of high-scale organizations.

Data Science 52

Data Science Scala Programming Language R (Programming) 52

ProjectPro

JUNE 4, 2015

The holistic view of Hadoop architecture gives prominence to Hadoop common, Hadoop YARN, Hadoop Distributed File Systems (HDFS ) and Hadoop MapReduce of the Hadoop Ecosystem. HDFS in Hadoop architecture provides high throughput access to application data and Hadoop MapReduce provides YARN based parallel processing of large data sets.

Hadoop 52

Hadoop Architecture IT Java 52

Silectis

JANUARY 3, 2021

Big Query Google’s cloud data warehouse. Data Architecture Data architecture is a composition of models, rules, and standards for all data systems and interactions between them. Data Catalog An organized inventory of data assets relying on metadata to help with data management.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer is someone proficient in a variety of programming languages and frameworks, such as Python, SQL, Scala, Hadoop, Spark, etc. One of the primary focuses of a Data Engineer's work is on the Hadoop data lakes. NoSQL databases are often implemented as a component of data pipelines.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Edureka

FEBRUARY 9, 2023

The role of Azure Data Engineer is in high demand in the field of data management and analytics. As an Azure Data Engineer, you will be in charge of designing, building, deploying, and maintaining data-driven solutions that meet your organization’s business needs.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

ProjectPro

DECEMBER 6, 2016

News on Hadoop-November 2016 Microsoft's Hadoop-friendly Azure Data Lake will be generally available in weeks. Microsoft's cloud-based Azure Data Lake will soon be available for big data analytic workloads. Azure Data Lake will have 3 important components -Azure Data Lake Analytics, Azure Data Lake Store and U-SQL.

Hadoop 52

Hadoop Data Lake BI Big Data 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

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. Data orchestration.

Big Data 44

Big Data NoSQL Data Lake Hadoop 44

ProjectPro

JANUARY 19, 2022

An Azure Data Engineer is a highly qualified expert responsible for integrating, transforming, and merging data from various structured and unstructured sources into a structure used to construct analytics solutions. The use of data has risen significantly in recent years.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Ascend.io

JULY 19, 2023

Ready or not, data mesh is fast becoming an indispensable part of the data landscape. As data leaders, the question isn’t if you’ll cross paths with this emerging architectural pattern. But something about data mesh feels different, doesn’t it? The question is when.

Data Governance 40

Data Governance Government Metadata Data 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 Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

OCTOBER 8, 2021

The bad news is, integrating data can become a tedious task, especially when done manually. Luckily, there are various data integration tools that support automation and provide a unified data view for more efficient data management. Data integration process. So, why is data integration such a big deal?

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. What is Big Data analytics?

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineer Salary by Experience (Entry-Level, Mid-Level, and Senior) Entry-Level Big Data Engineer Salary An entry-level position does not demand years of experience in Big Data technology. However, one should have an educational background and theoretical knowledge in data management.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52