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Analytics Vidhya
FEBRUARY 25, 2023
Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.
157 
KDnuggets
JUNE 27, 2022
If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?
160 
Precisely
MARCH 9, 2023
Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. It is often used as a foundation for enterprise data lakes.
59 
Snowflake
JUNE 21, 2024
Snowflake is now making it even easier for customers to bring the platform’s usability, performance, governance and many workloads to more data with Iceberg tables (now generally available), unlocking full storage interoperability. Iceberg tables provide compute engine interoperability over a single copy of data.
117 
Grouparoo
JANUARY 11, 2022
This article looks at the options available for storing and processing big data, which is too large for conventional databases to handle. There are two main options available, a data lake and a data warehouse. What is a Data Warehouse? What is a Data Lake?
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.
52 
U-Next
SEPTEMBER 7, 2022
The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Lake? . Athena on AWS. .
52 
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.
52 
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?
52 
Monte Carlo
AUGUST 25, 2023
That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider.
52 
Monte Carlo
APRIL 1, 2021
Over the past few years, data lakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.
52 
Data Engineering Podcast
JANUARY 14, 2017
Summary Do you wish that you could track the changes in your data the same way that you track the changes in your code? Pachyderm is a platform for building a data lake with a versioned file system. Interview with Daniel Whitenack Introduction How did you get started in the data engineering space?
100 
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? What is a Data lake?
52 
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? Let’s dive in.
64 
DataKitchen
AUGUST 19, 2021
Figure 3 shows an example processing architecture with data flowing in from internal and external sources. Each data source is updated on its own schedule, for example, daily, weekly or monthly. The data scientists and analysts have what they need to build analytics for the user. Each of the mastered data sets could be a domain.
Cloudera
MAY 19, 2021
Data Ingestion. The raw data is in a series of CSV files. We will firstly convert this to parquet format as most data lakes exist as object stores full of parquet files. RAPIDS is only supported on Pascal or newer NVIDIA GPUs. For AWS this means at least P3 instances. P2 GPU instances are not supported.
140 
ThoughtSpot
SEPTEMBER 6, 2023
And even when we manage to streamline the data workflow, those insights aren’t always accessible to users unfamiliar with antiquated business intelligence tools. That’s why ThoughtSpot and Fivetran are joining forces to decrease the amount of time, steps, and effort required to go from raw data to AI-powered insights.
105 
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.
96 
DataKitchen
AUGUST 9, 2021
The data industry has a wide variety of approaches and philosophies for managing data: Inman data factory, Kimball methodology, s tar schema , or the data vault pattern, which can be a great way to store and organize raw data, and more. Data mesh does not replace or require any of these.
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.
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.
40 
Towards Data Science
MAY 3, 2023
One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. Use VDK to build a data lake and merge multiple sources.
81 
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! constraints on data manipulation, security, privacy concerns, etc.)
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?
77 
DataKitchen
NOVEMBER 29, 2021
The rise of distributed data architectures like Data Mesh will combine with DataOps automation to give rise to Hub-Spoke architectures that deftly blend the benefits of centralization and decentralization. For example, a Hub-Spoke architecture could integrate data from a multitude of sources into a data lake.
98 
Cloudera
JANUARY 21, 2021
Of high value to existing customers, Cloudera’s Data Warehouse service has a unique, separated architecture. . Cloudera’s Data Warehouse service allows raw data to be stored in the cloud storage of your choice (S3, ADLSg2). When your IT admin registers an environment in CDP, a Data Lake is automatically deployed.
96 
Monte Carlo
NOVEMBER 22, 2024
Gone are the days of just dumping everything into a single database; modern data architectures typically use a combination of data lakes and warehouses. Think of your data lake as a vast reservoir where you store raw data in its original form—great for when you’re not quite sure how you’ll use it yet.
ProjectPro
FEBRUARY 8, 2023
But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. How Does AWS Glue Work?
98 
Data Engineering Podcast
JUNE 26, 2022
Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Data Engineering Podcast listeners can sign up for a free 2-week sandbox account, go to dataengineeringpodcast.com/tonic today to give it a try!
100 
Cloudera
SEPTEMBER 15, 2022
Mark: The first element in the process is the link between the source data and the entry point into the data platform. At Ramsey International (RI), we refer to that layer in the architecture as the foundation, but others call it a staging area, raw zone, or even a source data lake.
88 
Towards Data Science
NOVEMBER 16, 2023
Extract and Load This phase includes VDK jobs calling the Europeana REST API to extract raw data. You have just learned how to implement batch data processing in VDK! It only requires ingesting raw data, manipulating it, and, finally, using it for your purposes! link] Summary Congratulations!
73 
Ascend.io
AUGUST 31, 2023
Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape.
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.
76 
WeCloudData
OCTOBER 19, 2021
Resolution In order to meet the business requirements for a job market analysis platform & dashboard, WeCloudData helped the client leverage a suite of cloud platforms & tools to enable a data pipeline in multiple stages: Ingest job data from multiple sources and store the raw data in a cloud data lake Process the raw data with Python & (..)
52 
WeCloudData
OCTOBER 19, 2021
Resolution In order to meet the business requirements for a job market analysis platform & dashboard, WeCloudData helped the client leverage a suite of cloud platforms & tools to enable a data pipeline in multiple stages: Ingest job data from multiple sources and store the raw data in a cloud data lake Process the raw data with Python & (..)
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.
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.
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.
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?
88 
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.
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.
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.
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.
Edureka
JUNE 13, 2024
Hence, while using the Power Query, the raw data is grabbed, and the work is done using the Power Query engine. This has the advantage of offloading and optimizing processing in the Database itself as it minimizes the amount of data that is moved to Power BI, making the process faster and scalable.
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