Monte Carlo

NOVEMBER 14, 2023

In this piece, we break down popular Iceberg use cases, advantages and disadvantages, and its impact on data quality so you can make the table format decision that’s right for your team. Is your data lake a good fit for Iceberg? Without a central query log, your team can run the risk of data loss and lack of data governance.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Data Engineering Podcast

OCTOBER 5, 2020

Summary Analytical workloads require a well engineered and well maintained data integration process to ensure that your information is reliable and up to date. Building a real-time pipeline for your data lakes and data warehouses is a non-trivial effort, requiring a substantial investment of time and energy.

Data Integration 100

Data Integration Kafka Data Lake Machine Learning 100

Monte Carlo

MAY 14, 2024

It provides functionalities like upserts, incremental loads, and efficient change data capture, which are essential for systems requiring up-to-the-minute data freshness and quick query capabilities. As these new table formats have emerged, it’s changed the way data lakes are structured and managed.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

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 52

Data Integration Hadoop Data Warehouse Data Lake 52

Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. in alphabetical order: Apache Airflow, Azure Data Factory, DBT, Google DataForm, …).

Data Engineering 83

Data Engineering Data Engineer Engineering Data Lake 83

RandomTrees

FEBRUARY 6, 2024

Over the years, the field of data engineering has seen significant changes and paradigm shifts driven by the phenomenal growth of data and by major technological advances such as cloud computing, data lakes, distributed computing, containerization, serverless computing, machine learning, graph database, etc.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Knowledge Hut

MARCH 28, 2024

Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

Precisely

OCTOBER 16, 2023

The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale. BI platforms and data warehouses have been replaced by modern data lakes and cloud analytics solutions. Data integrity is also essential.

Cloud 70

Cloud Cloud Computing Metadata Data Governance 70

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance. Best Practice 2. Want to learn more?

Metadata 67

Metadata Data Governance Relational Database Government 67

Cloudera

MARCH 5, 2024

With built-in features like time travel, schema evolution, and streamlined data discovery, Iceberg empowers data teams to enhance data lake management while upholding data integrity. Available for cloud and now also for the data center. And that’s also what makes Cloudera so unique.

Data Lake 87

Data Lake Data Storage Government Kafka 87

Precisely

JUNE 6, 2023

The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Atif Salam noted that as recently as a year ago, the primary focus in many organizations was on ingesting data and building data lakes. She also emphasizes the need to automate as much as possible.

Data Architecture 52

Data Architecture Architecture Metadata Data Lake 52

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 59

IT Data Warehouse Data Governance Data Lake 59

Precisely

MARCH 27, 2023

Changing consumer preferences, along with a shift in focus toward digital channels, was driving the need for new approaches to data integrity at the company. million, and optimized data management processes and data integrity across its GTM processes, generating additional recurring annual savings of $6.6

Food 52

Food Consulting Pharmaceutical Data Lake 52

Databand.ai

AUGUST 30, 2023

Data silos: Legacy architectures often result in data being stored and processed in siloed environments, which can limit collaboration and hinder the ability to generate comprehensive insights. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

The Modern Data Company

MARCH 1, 2024

For today’s Chief Data Officers (CDOs) and data teams, the struggle is real. We’re drowning in data yet thirsting for actionable insights. We need a new approach, a paradigm shift that delivers data with the agility and efficiency of a speedboat – enter Data Products.

Database-centric 40

Database-centric Data Lake Government Retail 40

Knowledge Hut

NOVEMBER 2, 2023

Azure Data Ingestion Pipeline Create an Azure Data Factory data ingestion pipeline to extract data from a source (e.g., Azure SQL Database, Azure Data Lake Storage). Data Aggregation Working with a sample of big data allows you to investigate real-time data processing, big data project design, and data flow.

Data Engineering 52

Data Engineering Data Engineer Coding Project 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

Striim

NOVEMBER 15, 2023

Striim has been selected as one of Microsoft’s top strategic partners for Microsoft Fabric integration. Today, Striim is excited to announce its new low-latency, open-format data integration and streaming service for Microsoft Fabric. that can result in an increased TCO for the business.

Retail 52

Retail BI Data Warehouse Data Lake 52

Knowledge Hut

OCTOBER 30, 2023

This data and reports are generated and developed by Power BI developers. A Power BI developer is a business intelligence personnel who thoroughly understands business intelligence, data integration, data warehousing, modeling, database administration, and technical aspects of BI systems.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 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

DareData

JANUARY 30, 2023

AWS Glue: A fully managed data orchestrator service offered by Amazon Web Services (AWS). Talend Data Fabric: A comprehensive data management platform that includes a range of tools for data integration, data quality, and data governance. Introduction to Designing Data Lakes in AWS.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

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

Striim

FEBRUARY 12, 2024

For instance, analytical data may uncover customer preferences or seasonal product trends, helping teams in strategic planning. This data is commonly gathered through an ETL process and stored in a central location, such as a data lake.

Data Architecture 52

Data Architecture Architecture Data Data Lake 52

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 69

Process Data Lake Metadata Data Warehouse 69

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 52

Architecture Data Lake Data Warehouse Metadata 52

Monte Carlo

FEBRUARY 21, 2023

These include: Seamless integration A data fabric’s ability to integrate seamlessly with various APIs and software-delivery kits (SDKs) truly differentiates this framework from another type of data management: the ubiquitous data lake.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

These include: Seamless integration A data fabric’s ability to integrate seamlessly with various APIs and software-delivery kits (SDKs) truly differentiates this framework from another type of data management: the ubiquitous data lake.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

AltexSoft

AUGUST 22, 2022

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., to provide a unified view of all enterprise data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Databand.ai

JULY 19, 2023

The extracted data is often raw and unstructured and may come in various formats such as text, images, audio, or video. The extraction process requires careful planning to ensure data integrity. It’s crucial to understand the source systems and their structure, as well as the type and quality of data they produce.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Knowledge Hut

OCTOBER 4, 2023

It is because they help you generate dynamic reports and feature data modeling, complete data comparison, and more. Understanding Power BI Requirements As I have mentioned before, Power BI is a revolutionary, remarkable program that enables high-speed data integration and the creation of plenty of reports.

BI 52

BI Systems Raw Data Business Intelligence 52

Databand.ai

JULY 28, 2022

In this very simplified example, we can see an ELT: Some pipeline tasks, probably running by Airflow , are scraping external data sources and collecting data from there. Those tasks are saving the extracted data in the data lake (or warehouse or lakehouse). This technique focuses directly on the data (vs.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52