ProjectPro

FEBRUARY 8, 2023

It offers users a data integration tool that organizes data from many sources, formats it, and stores it in a single repository, such as data lakes, data warehouses, etc., Glue uses ETL jobs for extracting data from various AWS cloud services and integrating it into data warehouses and lakes.

AWS 98

AWS Scala Metadata Data Lake 98

Data Engineering Weekly

MARCH 24, 2023

Data engineering starts to add value to the business by capturing events at each step of the business process. The events are then further enriched and analyzed to bring visibility to business operations. Architectural patterns for Data Creation There are three types of architecture patterns in data creation.

Engineering 67

Engineering Data Transportation Database 67

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

AltexSoft

JUNE 26, 2023

Data collection methods and tools: how to collect For centuries, people have been collecting data manually. Even now, in the era of ChatGPT , we still fill out paper-based documents and type numbers and words into an Excel file to capture events and record observations. The difference between data warehouses, lakes, and marts.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

OCTOBER 28, 2015

The major difference between Sqoop and Flume is that Sqoop is used for loading data from relational databases into HDFS while Flume is used to capture a stream of moving data. Table of Contents Hadoop ETL tools: Sqoop vs Flume-Comparison of the two Best Data Ingestion Tools What is Sqoop in Hadoop?

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 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

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

AltexSoft

AUGUST 22, 2022

What is data fabric? A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., How data fabric works.

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

Netflix Tech

DECEMBER 14, 2018

A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

Media 94

Media Database Metadata Data Schemas 94

ProjectPro

NOVEMBER 15, 2021

It serves as a distributed processing engine for both categories of data streams: unbounded and bounded. Support for stream and batch processing, comprehensive state management, event-time processing semantics, and consistency guarantee for the state are just a few of Flink's capabilities. However, Trino is not limited to HDFS access.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

JUNE 23, 2016

Thus, this solution is not practically recommended and this is when Apache Sqoop comes to the rescues of users that allows users to import data on HDFS. Apache Sqoop is a lifesaver for people facing challenges with moving data out of a data warehouse into the Hadoop environment. directly into HDFS or Hive or HBase.

Hadoop 40

Hadoop MySQL Relational Database Java 40

ProjectPro

AUGUST 9, 2018

No impact Database Engine MySQL, Oracle DB, SQL Server, Amazon Aurora, Postgre SQL Redshift NoSQL Primary Usage Feature Conventional Databases Data warehouse Database for dynamically modified data Multi A-Z Replication Additional Service Manual In-built 7. Maintenance Window 30 minutes every week.

AWS 40

AWS Amazon Web Services Cloud Computing Database 40

Cloudera

JUNE 16, 2022

The DevOps/app dev team wants to know how data flows between such entities and understand the key performance metrics (KPMs) of these entities. For governance and security teams, the questions revolve around chain of custody, audit, metadata, access control, and lineage.

Kafka 84

Kafka Manufacturing Data Lake SQL 84

AltexSoft

MAY 12, 2023

Structured data is formatted in tables, rows, and columns, following a well-defined, fixed schema with specific data types, relationships, and rules. A fixed schema means the structure and organization of the data are predetermined and consistent. MongoDB, Cassandra), and big data processing frameworks (e.g.,

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

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