Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

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

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Cloudera

SEPTEMBER 9, 2022

The promise of a modern data lakehouse architecture. Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested.

Architecture 100

Architecture Metadata Unstructured Data Machine Learning 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. workflows (Airflow, Prefect, Dagster, etc.)

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Rockset

AUGUST 4, 2021

Organizations that depend on data for their success and survival need robust, scalable data architecture, typically employing a data warehouse for analytics needs. Snowflake is often their cloud-native data warehouse of choice. Data ingestion must be performant to handle large amounts of data.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Data Lake 40

Data Engineering Podcast

NOVEMBER 20, 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 130

Data Lake Data Ingestion MongoDB Scala 130

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

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.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

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. That’s where our friends at Ascend.io

Data Lake 100

Data Lake Food MongoDB Scala 100

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

RandomTrees

NOVEMBER 29, 2023

So, here the argument on what to use and where to use become an important topic to be considered and here we can rather focus on some of the adaptable and sensitive models and we begin to consider the data vaults’ technique. So, what is a data vault model or modelling approach?

Data Warehouse 52

Data Warehouse Data Lake Database-centric Data Cleanse 52

Data Engineering Podcast

NOVEMBER 6, 2022

RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control.

MongoDB 130

MongoDB Scala MySQL Data Lake 130

Cloudera

JUNE 30, 2022

Over the past decade, Cloudera has enabled multi-function analytics on data lakes through the introduction of the Hive table format and Hive ACID. Companies, on the other hand, have continued to demand highly scalable and flexible analytic engines and services on the data lake, without vendor lock-in.

Data Lake 86

Data Lake Business Intelligence Metadata Data Warehouse 86

Data Engineering Podcast

NOVEMBER 13, 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. What are some of the characteristics of the data that you work with?

MongoDB 100

MongoDB Scala Google Cloud MySQL 100

Data Engineering Podcast

OCTOBER 30, 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.

Engineering 100

Engineering MongoDB Scala MySQL 100

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

Data Engineering Podcast

SEPTEMBER 25, 2022

Mention the podcast to get a free "In Data We Trust World Tour" t-shirt. RudderStack helps you build a customer data platform on your warehouse or data lake. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability.

Food 100

Food MongoDB Scala MySQL 100

Cloudera

AUGUST 9, 2022

Data platforms are no longer skunkworks projects or science experiments. As customers import their mainframe and legacy data warehouse workloads, there is an expectation on the platform that it can meet, if not exceed, the resilience of the prior system and its associated dependencies.

Data Lake 77

Data Lake Data Warehouse Architecture Data Ingestion 77

Ascend.io

JANUARY 2, 2024

These are the interfaces where the pipeline taps into various systems to acquire data. The sources of data can be incredibly diverse, ranging from data warehouses, relational databases, and web analytics to CRM platforms, social media tools, and IoT device sensors. best suit our processed data?

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

AltexSoft

MARCH 30, 2023

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. The relatively new storage architecture powering Databricks is called a data lakehouse. Databricks lakehouse platform architecture.

Scala 64

Scala Data Lake BI Google Cloud 64

Towards Data Science

JULY 24, 2023

A data engineering manager at a Fortune 500 company expressed the pain of on-prem limitations to me by saying: “Our analysts were unable to run the queries they wanted to run when they wanted to run them. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 96

Metadata Data Warehouse Data Lake Data Science 96

Edureka

FEBRUARY 9, 2023

As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical. Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions. It is also crucial to have experience with data ingestion and transformation.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

NOVEMBER 2, 2023

To create effective and scalable data pipelines, data storage solutions, and data analytics environments, they work with a variety of Azure services and tools. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Data Engineering Podcast

JULY 3, 2022

In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability.

Data Integration 100

Data Integration MongoDB Scala MySQL 100

Data Engineering Podcast

OCTOBER 2, 2022

Mention the podcast to get a free "In Data We Trust World Tour" t-shirt. RudderStack helps you build a customer data platform on your warehouse or data lake. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability.

IT 100

IT Food PostgreSQL MongoDB 100

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Data-first companies have embraced data platforms as an effective way to aggregate, operationalize, and democratize data at scale across the organization. Snowflake, a cloud data warehouse, is a popular choice among data teams when it comes to quickly scaling up a data platform.

Building 52

Building BI Data Lake Data Governance 52

Data Engineering Podcast

AUGUST 6, 2022

Summary The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. RudderStack helps you build a customer data platform on your warehouse or data lake.

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

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

Towards Data Science

APRIL 3, 2023

As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Hightouch interrupted SaaS eating the world in an attempt to shift the center of gravity to the data warehouse. Other common light transformations done within the ingestion phase are data formatting and deduplication.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

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. Read our articles on structured vs unstructured data and unstructured data to learn more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Cloudera

JUNE 16, 2022

This blog aims to answer two questions as illustrated in the diagram below: How have stream processing requirements and use cases evolved as more organizations shift to “streaming first” architectures and attempt to build streaming analytics pipelines? Faster data ingestion: streaming ingestion pipelines.

Kafka 86

Kafka Manufacturing Data Lake SQL 86

Cloudera

AUGUST 26, 2020

Hello Dinesh, thank you for joining us for Part II of our Q&A on streaming data. Can you talk a bit about how businesses best use Flink within a streaming architecture and what is it about the solution that promotes low latency processing of high-volume streaming data?

Banking 82

Banking Data Ingestion Kafka Data Lake 82

Rockset

JULY 28, 2022

As capable as it is, there are still instances where MongoDB alone can't satisfy all of the requirements for an application, so getting a copy of the data into another platform via a change data capture (CDC) solution is required. If this is not already part of your architecture then it may add another layer of complexity and cost.

MongoDB 52

MongoDB Kafka NoSQL Data Lake 52

Monte Carlo

JULY 18, 2023

A data engineering manager at a Fortune 500 company expressed the pain of on-prem limitations to me by saying: “Our analysts were unable to run the queries they wanted to run when they wanted to run them. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 52

Metadata Data Warehouse Data Lake Government 52

ProjectPro

FEBRUARY 8, 2023

Let us dive deeper into this data integration solution by AWS and understand how and why big data professionals leverage it in their data engineering projects. 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.,

AWS 98

AWS Scala Metadata Data Lake 98

Knowledge Hut

APRIL 25, 2023

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52