Monte Carlo

AUGUST 6, 2024

Data warehouses are the centralized repositories that store and manage data from various sources. They are integral to an organization’s data strategy, ensuring data accessibility, accuracy, and utility. However, beneath their surface lies a host of invisible risks embedded within the data warehouse layers.

Data Warehouse 52

Data Warehouse Raw Data Machine Learning BI 52

Christophe Blefari

JUNE 21, 2024

Snowflake was founded in 2012 around its data warehouse product, which is still its core offering, and Databricks was founded in 2013 from academia with Spark co-creator researchers, becoming Apache Spark in 2014. Databricks is focusing on simplification (serverless, auto BI 2 , improved PySpark) while evolving into a data warehouse.

Metadata 147

Metadata Data Warehouse BI MySQL 147

Maxime Beauchemin

AUGUST 28, 2017

Consensus seeking Whether you think that old-school data warehousing concepts are fading or not, the quest to achieve conformed dimensions and conformed metrics is as relevant as it ever was. The data warehouse needs to reflect the business, and the business should have clarity on how it thinks about analytics.

Data Engineering 174

Data Engineering Data Engineer Engineering Software Engineering 174

Analytics Vidhya

FEBRUARY 25, 2023

The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

Cloud Storage 162

Cloud Storage Data Lake Cloud Unstructured Data 162

Maxime Beauchemin

JANUARY 7, 2018

While business rules evolve constantly, and while corrections and adjustments to the process are more the rule than the exception, it’s important to insulate compute logic changes from data changes and have control over all of the moving parts. But how do we model this in a functional data warehouse without mutating data?

Data Engineering 183

Data Engineering Data Process Data Engineer Process 183

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. There are times when the data is structured , but it is often messy since it is ingested directly from the data source. What is Data Warehouse? . Data Warehouse in DBMS: .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Sync Computing

NOVEMBER 7, 2024

Data volume and velocity, governance, structure, and regulatory requirements have all evolved and continue to. Despite these limitations, data warehouses, introduced in the late 1980s based on ideas developed even earlier, remain in widespread use today for certain business intelligence and data analysis applications.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Manufacturing 110

Manufacturing Data Warehouse Kafka Retail 110

Monte Carlo

APRIL 4, 2023

Data mesh vs data warehouse is an interesting framing because it is not necessarily a binary choice depending on what exactly you mean by data warehouse (more on that later). Despite their differences, however, both approaches require high-quality, reliable data in order to function. What is a Data Mesh?

Data Warehouse 52

Data Warehouse Data Governance Data Architecture 52

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Snowflake

NOVEMBER 30, 2023

At TCS , we help companies shift their enterprise data warehouse (EDW) platforms to the cloud as well as offering IT services. We’re extremely familiar with just how tricky a cloud migration can be, especially when it involves moving historical business data. How many tables and views will be migrated, and how much raw data?

Data Warehouse 116

Data Warehouse Banking Data Cloud 116

Monte Carlo

NOVEMBER 21, 2024

Just make sure you have enough processes in place to prevent data silos! Data Lakehouse Pattern Data lakehouses are the sporks of architectural patterns – combining the best parts of data warehouses with data lakes. The data lakehouse has got you covered!

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

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 52

Data Lake Data Warehouse Cloud Hadoop 52

DataKitchen

AUGUST 19, 2021

Each data source is updated on its own schedule, for example, daily, weekly or monthly. The DataKitchen Platform ingests data into a data lake and runs Recipes to create a data warehouse leveraged by users and self-service data analysts. Let’s consider how to break up our architecture into data mesh domains.

Pharmaceutical 98

Pharmaceutical Data Lake Data Warehouse Raw Data 98

Data Engineering Podcast

DECEMBER 11, 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.

Data Warehouse 100

Data Warehouse Raw Data Data Lake BI 100

Towards Data Science

FEBRUARY 6, 2024

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

Systems 98

Systems Raw Data Metadata Data Cleanse 98

Christophe Blefari

APRIL 21, 2023

A lot of data teams embraced dbt, or at least the SQL with engineering practices to transform data in cloud data warehouses. As introduction Tristan gives the original vision of dbt that became mainstream, today. In dbt Core 1.5

Raw Data 130

Raw Data Data SQL Datasets 130

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Towards Data Science

NOVEMBER 16, 2023

ELT comprises three main phases: Extract (E) : data is extracted from multiple sources in different formats, both structured and unstructured. Load (L) : data is loaded into a target destination, such as a data warehouse. Extract and Load This phase includes VDK jobs calling the Europeana REST API to extract raw data.

Data Process 98

Data Process Process Raw Data Data 98

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse?

Engineering 105

Engineering Raw Data Data Science Machine Learning 105

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers. What is Real Time Data Warehousing?

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

Cloudera

DECEMBER 12, 2018

After having rebuilt their data warehouse, I decided to take a little bit more of a pointed role, and I joined Oracle as a database performance engineer. I spent eight years in the real-world performance group where I specialized in high visibility and high impact data warehousing competes and benchmarks.

Data Warehouse 42

Data Warehouse Relational Database Hadoop Database 42

Data Engineering Podcast

DECEMBER 16, 2019

You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse? Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Knowledge Hut

MARCH 7, 2024

The greatest data processing challenge of 2024 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data. Inability to process large volumes of data Out of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it.

Big Data 98

Big Data Bytes Data Governance Raw Data 98

dbt Developer Hub

APRIL 19, 2023

The goal of dimensional modeling is to take raw data and transform it into Fact and Dimension tables that represent the business. Part 7: Consume dimensional model ​ Finally, we can consume our dimensional model by connecting to our data warehouse to our Business Intelligence (BI) tools such as Tableau, Power BI, and Looker.

Building 145

Building PostgreSQL BI Database 145

Cloudera

JANUARY 21, 2021

While cloud-native, point-solution data warehouse services may serve your immediate business needs, there are dangers to the corporation as a whole when you do your own IT this way. Cloudera Data Warehouse (CDW) is here to save the day! CDW is an integrated data warehouse service within Cloudera Data Platform (CDP).

IT 94

IT Data Lake Data Warehouse Cloud Storage 94

Ripple Engineering

JULY 9, 2024

After evaluating numerous data solution providers, Databricks stood out due to its seamless performance and lakehouse capabilities, which offer the best of both data lakes and data warehouses. This vital information then streams to the XRPL Data Extractor App. Why Databricks Emerged as the Top Contender 1.

Hadoop 96

Hadoop Data Lake Machine Learning Raw Data 96

Hevo

FEBRUARY 15, 2023

After the hustle and bustle of extracting data from multiple sources, you have finally loaded all your data to a single source of truth like the Snowflake data warehouse. However, data modeling is still challenging and critical for transforming your raw data into any analysis-ready form to get insights.

Data Pipeline 59

Data Pipeline Building Raw Data Data Warehouse 59

Towards Data Science

MAY 3, 2023

VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. You can use VDK to build data lakes and ingest raw data extracted from different sources, including structured, semi-structured, and unstructured data.

Data Lake 98

Data Lake SQL Data Data Warehouse 98

Grouparoo

AUGUST 3, 2021

The mission of many data teams is a very simple one. They seek to use data to help the business take smarter actions. The input is raw data from everywhere that touches the business. How can we best get the data into a usable form? The end goal of both is to have data in a data warehouse ready to be leveraged.

Raw Data 52

Raw Data Data Warehouse SQL Coding 52

Workfall

SEPTEMBER 18, 2023

Meet Airbyte, the data magician that turns integration complexities into child’s play. In this digital era, businesses thrive on data, and making this data dance harmoniously with your analytics tools is crucial. Airbyte ensures that you don’t miss out on those insights due to tangled data integration processes.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

Monte Carlo

FEBRUARY 25, 2025

ELT: When to Transform Your Data ETL (Extract, Transform, Load) ELT (Extract, Load, Transform) Which One Should You Choose? Batch vs. Stream Processing: How to Move Your Data Batch Processing Stream Processing Which One Should You Choose? Data Lakes vs. Data Warehouses: Where Should Your Data Live?

Designing 52

Designing Data Engineering Data Engineer Engineering 52

Grouparoo

DECEMBER 14, 2021

ETL, or Extract, Transform, Load, is a process that involves extracting data from different data sources , transforming it into more suitable formats for processing and analytics, and loading it into the target system, usually a data warehouse. ETL data pipelines can be built using a variety of approaches.

Process 52

Process Raw Data Data Warehouse Data Pipeline 52

Towards Data Science

JULY 26, 2024

Back to Data in the 21st Century Thinking back at my own experiences, the philosophy of most big data engineering projects I’ve worked on was similar to that of Multics. For example, there was a project where we needed to automate standardising the raw data coming in from all our clients. That was it.

Data Engineering 75

Data Engineering Data Engineer Engineering Data Warehouse 75

Cloud Academy

JUNE 7, 2022

The data extraction process The first step of modeling and using data begins with extracting it from different sources and putting it in a library where it can be assessed: the Data Warehouse. In some cases, raw data is extracted and placed in the staging area; in others, a few transformations need to be performed.

Cloud 52

Cloud Data Warehouse Raw Data Business Intelligence 52

Hevo

FEBRUARY 21, 2023

As data volumes continue to grow, organizations seek ways to make sense of it all, and data warehouses are at the center. BigQuery is a popular cloud-based data warehouse that allows for powerful analytics and querying at scale. This is […]

Raw Data 52

Raw Data Data Warehouse Data Cloud 52

Towards Data Science

FEBRUARY 9, 2024

Data Store Another significant change from 2021 to 2024 lies in the shift from “Data Warehouse” to “Data Store,” acknowledging the expanding database horizon, including the rise of Data Lakes. Their robust core offering seamlessly integrates data warehouses with data-hungry applications.

Building 98

Building Transportation Data Lake Metadata 98