Data Integration, Data Lake, Metadata and NoSQL

5 Layers of Data Lakehouse Architecture Explained

Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Metadata layer 4. …ok, so maybe they don’t say that. But they should! Storage layer 3. API layer 5.

Architecture

Architecture Data Lake Metadata Unstructured Data

Data Lakehouse Architecture Explained: 5 Layers

Monte Carlo

JANUARY 5, 2024

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Metadata layer 4. …ok, so maybe they don’t say that. But they should! Storage layer 3. API layer 5.

Architecture

Architecture Data Lake Metadata Unstructured Data

DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

AUGUST 30, 2023

In a DataOps architecture, it’s crucial to have an efficient and scalable data ingestion process that can handle data from diverse sources and formats. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture

Architecture Data Ingestion Data Governance Data Cleanse

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Data Engineering Glossary

Silectis

JANUARY 3, 2021

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. Database A collection of structured data.

Data Engineering

Data Engineering Data Engineer Engineering Hadoop

Data Architect: Role Description, Skills, Certifications and When to Hire

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect

Data Architect Certification Generalist Big Data

Data Lake vs Data Warehouse - Working Together in the Cloud

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake

Data Lake Data Warehouse Cloud Hadoop

IBM InfoSphere vs Oracle Data Integrator vs Xplenty and Others: Data Integration Tools Compared

AltexSoft

OCTOBER 8, 2021

What’s more, that data comes in different forms and its volumes keep growing rapidly every day — hence the name of Big Data. The good news is, businesses can choose the path of data integration to make the most out of the available information. Data integration in a nutshell. Data integration process.

Data Integration

Data Integration Hadoop Data Warehouse Data Lake

Data Lakehouse: Concept, Key Features, and Architecture Layers

AltexSoft

NOVEMBER 10, 2021

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. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture

Architecture Data Lake Data Warehouse Metadata

Data Collection for Machine Learning: Steps, Methods, and Best Practices

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

Data Collection Machine Learning Unstructured Data Non-relational Database

ELT Process: Key Components, Benefits, and Tools to Build ELT Pipelines

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. ELT process steps. Enrichment.

Process

Process Building Raw Data Data Lake

Data Scientist vs Data Engineer: Differences and Why You Need Both

AltexSoft

OCTOBER 30, 2021

Managing data and metadata. There are different ways how data can be stored: a data warehouse, numerous data lakes and data hubs , etc. Data engineers control how data is stored and structured within those locations. Providing data access tools. Data warehousing.

Data Engineering

Data Engineering Data Engineer Engineering Machine Learning

The Good and the Bad of Hadoop Big Data Framework

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

Hadoop Big Data Google Cloud NoSQL

Data Virtualization: Process, Components, Benefits, and Available Tools

AltexSoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Process

Process Data Lake Metadata Data Warehouse

20 Best Open Source Big Data Projects to Contribute on GitHub

ProjectPro

NOVEMBER 15, 2021

Delta Lake Source: Github Delta Lake is an open-source project that allows you to create a Lakehouse design based on data lakes. On top of existing data lakes like S3, ADLS, GCS, and HDFS, Delta Lake enables ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

Big Data

Big Data Project Metadata Programming Language

100+ Big Data Interview Questions and Answers 2023

ProjectPro

JANUARY 31, 2023

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.

Big Data

Big Data Hadoop AWS Relational Database

Unstructured Data: Examples, Tools, Techniques, and Best Practices

AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. A loose schema allows for some data structure flexibility while maintaining a general organization. Hadoop, Apache Spark).

Unstructured Data

Unstructured Data NoSQL Hadoop Data Lake

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

MARCH 14, 2023

It must collect, analyze, and leverage large amounts of customer data from various sources, including booking history from a CRM system, search queries tracked with Google Analytics, and social media interactions. Built around a cloud data warehouse, data lake, or data lakehouse. Designed to be modular.

IT

IT Data Warehouse Data Governance Data Lake

Data Engineering Digest

5 Layers of Data Lakehouse Architecture Explained

Data Lakehouse Architecture Explained: 5 Layers

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DataOps Architecture: 5 Key Components and How to Get Started

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Data Architect: Role Description, Skills, Certifications and When to Hire

Data Lake vs Data Warehouse - Working Together in the Cloud

IBM InfoSphere vs Oracle Data Integrator vs Xplenty and Others: Data Integration Tools Compared

Data Lakehouse: Concept, Key Features, and Architecture Layers

Data Collection for Machine Learning: Steps, Methods, and Best Practices

ELT Process: Key Components, Benefits, and Tools to Build ELT Pipelines

Data Scientist vs Data Engineer: Differences and Why You Need Both

The Good and the Bad of Hadoop Big Data Framework

Data Virtualization: Process, Components, Benefits, and Available Tools

20 Best Open Source Big Data Projects to Contribute on GitHub

100+ Big Data Interview Questions and Answers 2023

Unstructured Data: Examples, Tools, Techniques, and Best Practices

Top 100 Hadoop Interview Questions and Answers 2023

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

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