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. What is a data lake?

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Data Engineering Podcast

MAY 15, 2022

Summary Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional layers of difficulty. When is a data lake architecture the wrong choice?

Data Lake 100

Data Lake Building BI Architecture 100

DareData

JULY 5, 2023

Learn how we build data lake infrastructures and help organizations all around the world achieving their data goals. In today's data-driven world, organizations are faced with the challenge of managing and processing large volumes of data efficiently.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

phData: Data Engineering

APRIL 4, 2023

Fivetran today announced support for Amazon Simple Storage Service (Amazon S3) with Apache Iceberg data lake format. Amazon S3 is an object storage service from Amazon Web Services (AWS) that offers industry-leading scalability, data availability, security, and performance.

Data Lake 52

Data Lake Amazon Web Services Data Cleanse Data Warehouse 52

Data Engineering Podcast

JULY 22, 2019

Summary The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of data lakes as a solution for managing storage and access.

Data Lake 100

Data Lake Hadoop Data Architecture 100

Data Engineering Podcast

NOVEMBER 11, 2018

Summary A data lake can be a highly valuable resource, as long as it is well built and well managed. In this episode Yoni Iny, CTO of Upsolver, discusses the various components that are necessary for a successful data lake project, how the Upsolver platform is architected, and how modern data lakes can benefit your organization.

Data Lake 100

Data Lake Building Kafka Cloud 100

Confessions of a Data Guy

JANUARY 12, 2023

Anyone who’s been roaming around the forest of Data Engineering has probably run into many of the newish tools that have been growing rapidly around the concepts of Data Warehouses, Data Lakes, and Lake Houses … the merging of the old relational database functionality with TB and PB level cloud-based file storage systems.

Data Lake 162

Data Lake Relational Database Data Warehouse Data Engineering 162

Data Engineering Podcast

MAY 29, 2022

Summary The latest generation of data warehouse platforms have brought unprecedented operational simplicity and effectively infinite scale. In order to ensure that you can explore and analyze your data without spending money on inefficient queries Mingsheng Hong and Zheng Shao created Bluesky Data.

Cloud 100

Cloud Data Warehouse Data Lake Data 100

Christophe Blefari

JANUARY 20, 2024

Data engineering inherits from years of data practices in US big companies. Hadoop initially led the way with Big Data and distributed computing on-premise to finally land on Modern Data Stack — in the cloud — with a data warehouse at the center. This is often linked to real-time.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Data Engineering Podcast

MARCH 27, 2022

Summary Data governance is a practice that requires a high degree of flexibility and collaboration at the organizational and technical levels. The growing prominence of cloud and hybrid environments in data management adds additional stress to an already complex endeavor.

Data Governance 130

Data Governance Government Cloud Building 130

Data Engineering Podcast

FEBRUARY 19, 2023

Summary Cloud data warehouses have unlocked a massive amount of innovation and investment in data applications, but they are still inherently limiting. Because of their complete ownership of your data they constrain the possibilities of what data you can store and how it can be used.

IT 147

IT Data Lake Metadata Data Warehouse 147

Cloudera

OCTOBER 13, 2020

On September 24, 2019, Cloudera launched CDP Public Cloud (CDP-PC) as the first step in delivering the industry’s first Enterprise Data Cloud. Over the past year, we’ve not only added Azure as a supported cloud platform, but we have improved the orginal services while growing the CDP-PC family significantly: Improved Services.

Cloud 90

Cloud Data Warehouse AWS NoSQL 90

Knowledge Hut

MARCH 28, 2024

Azure Data Engineer Career Demands & Benefits Azure has become one of the most powerful platforms in the industry, where Microsoft offers a variety of data services and analytics tools. As a result, organizations are looking to capitalize on cloud-based data solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Central to this transformation are two shifts.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Cloudera

JANUARY 19, 2022

Gartner® recognized Cloudera in three recent reports – Magic Quadrant for Cloud Database Management Systems (DBMS), Critical Capabilities for Cloud Database Management Systems for Analytical Use Cases and Critical Capabilities for Cloud Database Management Systems for Operational Use Cases.

Database 86

Database Cloud Data Warehouse Data Lake 86

Cloudera

NOVEMBER 12, 2020

Cloud has given us hope, with public clouds at our disposal we now have virtually infinite resources, but they come at a different cost – using the cloud means we may be creating yet another series of silos, which also creates unmeasurable new risks in security and traceability of our data. A solution.

Cloud 101

Cloud Data Warehouse Banking Data 101

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 89

Data Lake Data Warehouse BI SQL 89

Data Engineering Podcast

OCTOBER 21, 2019

In this episode Dipti Borkar explains how the emerging category of data orchestration tools fills this need, some of the existing projects that fit in this space, and some of the ways that they can work together to simplify projects such as cloud migration and hybrid cloud environments.

Cloud 100

Cloud Data Lake Hadoop Programming Language 100

Data Engineering Podcast

DECEMBER 11, 2022

Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. RudderStack helps you build a customer data platform on your warehouse or data lake. RudderStack helps you build a customer data platform on your warehouse or data lake.

Database 100

Database MySQL Data Lake MongoDB 100

Towards Data Science

JANUARY 16, 2024

Evolution of the data landscape 1980s — Inception Relational databases came into existence. Result: Data warehouse was born. Data volumes started to grow. Result: The concept of Massively Parallel Processing (MPP) was introduced — data distributed across clusters. The concept of `Data Marts` was introduced.

Data Warehouse 69

Data Warehouse Data Architecture Relational Database NoSQL 69

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, 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.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, 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.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Data Engineering Podcast

DECEMBER 4, 2022

Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. RudderStack helps you build a customer data platform on your warehouse or data lake. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Lake 100

Data Lake MongoDB MySQL Data Warehouse 100

François Nguyen

MARCH 22, 2021

It will be illustrated with our technical choices and the services we are using in the Google Cloud Platform. With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Cloudera

SEPTEMBER 2, 2020

Recently, Cloudera announced the release of Cloudera CDP Private Cloud, delivering the final component of our hybrid cloud strategy. CDP Private Cloud offers benefits of a public cloud architecture—autoscaling, isolation, agile provisioning, etc.—in Migrate from large on-premises CDH/HDP clusters to CDP Private Cloud.

Cloud 65

Cloud Data Warehouse Data Lake Machine Learning 65

Snowflake

SEPTEMBER 14, 2023

The Snowflake Data Cloud gives you the flexibility to build a modern architecture of choice to unlock value from your data. Snowflake was built from the ground up in the cloud. Snowflake’s platform provides industry-leading features that ensure the highest standards of governance for your account, users, and data.

Data Architecture 98

Data Architecture Architecture Hadoop Data Warehouse 98

Data Engineering Podcast

OCTOBER 16, 2022

Summary The "data lakehouse" architecture balances the scalability and flexibility of data lakes with the ease of use and transaction support of data warehouses. Mention the podcast to get a free "In Data We Trust World Tour" t-shirt. Data teams are increasingly under pressure to deliver.

Data Lake 100

Data Lake Food MongoDB Scala 100

Knowledge Hut

NOVEMBER 17, 2023

To provide end users with a variety of ready-made models, Azure Data engineers collaborate with Azure AI services built on top of Azure Cognitive Services APIs. Additionally, they convert data into formats that can be used and store it effectively and securely in the Azure cloud.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

U-Next

MARCH 1, 2023

Let’s discuss some of the key responsibilities of a Data Engineer: Data Engineers are responsible for deploying the solutions they design and build, and they should have a good knowledge of cloud platforms like AWS, Azure, etc. Analyzing this data often involves Machine Learning, a part of Data Science.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Data Engineering Podcast

AUGUST 20, 2021

Summary Data lakes have been gaining popularity alongside an increase in their sophistication and usability. Despite improvements in performance and data architecture they still require significant knowledge and experience to deploy and manage. The data you’re looking for is already in your data warehouse and BI tools.

Building 100

Building Data Lake Data Warehouse SQL 100

Cloudera

MARCH 17, 2023

The table format provides the necessary structure for the unstructured data that is missing in a data lake, using a schema or metadata definition, to bring it closer to a data warehouse. Some of the popular table formats are Apache Iceberg, Delta Lake, Hudi, and Hive ACID.

Education 60

Education Unstructured Data Data Lake Data Warehouse 60

Cloudera

JUNE 2, 2022

Over the last few years, we have had a front-row seat in our customers’ hybrid cloud journey as they expand their data estate across the edge, on-premise, and multiple cloud providers. This need has generated a market opportunity for a universal data distribution service. What is the modern data stack?

Systems 98

Systems Data Lake Google Cloud Data Collection 98

Data Engineering Podcast

NOVEMBER 4, 2021

Summary The precursor to widespread adoption of cloud data warehouses was the creation of customer data platforms. Acting as a centralized repository of information about how your customers interact with your organization they drove a wave of analytics about how to improve products based on actual usage data.

Data Warehouse 100

Data Warehouse Business Intelligence ETL System Data Lake 100

Data Engineering Podcast

DECEMBER 19, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Systems 100

Systems Building Metadata Data Warehouse 100

Christophe Blefari

MARCH 4, 2023

How to run dbt with BigQuery in GitHub Actions — When you're starting with dbt you don't need any orchestrator or dbt Cloud, a CI/CD do it for sure. Tabular is a cloud offer using Apache Iceberg. Some company also call it a lakehouse or a data lake, but the word shift is enough interesting to notice.

Machine Learning 130

Machine Learning AWS Data Data Lake 130

Christophe Blefari

FEBRUARY 24, 2023

The current data state is heavily dependent on infrastructure, wether it's cloud, on-premise or semi-related we need to understand where the data lands and where the code runs. At the moment the best way to develop in your data warehouse is still in the query editor of BigQuery / Snowflake / etc.,

Kafka 130

Kafka Data Lake Data Storage Data 130

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