Dataversity

MAY 26, 2022

A data lake becomes a data swamp in the absence of comprehensive data quality validation and does not offer a clear link to value creation. Organizations are rapidly adopting the cloud data lake as the data lake of choice, and the need for validating data in real time has become critical.

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Data Lakes Machine Learning Machine Learning Data Quality 52

Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

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Data Lakes Data Warehouse Data Quality Data Governance 59

Dataversity

MARCH 24, 2021

Enterprises migrating on-prem data environments to the cloud in pursuit of more robust, flexible, and integrated analytics and AI/ML capabilities are fueling a surge in cloud data lake implementations. The post How to Ensure Your New Cloud Data Lake Is Secure appeared first on DATAVERSITY.

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Dataversity

AUGUST 19, 2022

The data we produce and manage is growing in scale and demands careful consideration of the proper data framework for the job. There’s no one-size-fits-all data architecture, and […]. The post Data Lakes Are Dead: Evolving Your Company’s Data Architecture appeared first on DATAVERSITY.

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Data Lakes Data Governance Data Quality 52

databricks

MAY 9, 2023

As organizations are maturing their data infrastructure and accumulating more data than ever before in their data lakes, Open and Reliable table formats.

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Data Lakes Data Engineering Data Engineer Data Engineering 231

Data Science Blog

MAY 15, 2023

tl;dr Ein Data Lakehouse ist eine moderne Datenarchitektur, die die Vorteile eines Data Lake und eines Data Warehouse kombiniert. Die Definition eines Data Lakehouse Ein Data Lakehouse ist eine moderne Datenspeicher- und -verarbeitungsarchitektur, die die Vorteile von Data Lakes und Data Warehouses vereint.

Data Warehouse 147

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Data Science Dojo

JANUARY 12, 2023

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

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Data Science Blog

FEBRUARY 7, 2023

Was gerade zum Trend wird, ist der Aufbau eines Data Lakehouses. Ein Lakehouse inkludiert auch clevere Art und Weise auch einen Data Lake. Ein Data Lake ist dann sowas wie die eine böse Schublade, die man eigentlich gar nicht haben möchte, aber in die man dann alle Briefe, Dokumente usw.

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ODSC - Open Data Science

MAY 7, 2024

Beyond his technical achievements, James is a sought-after speaker and is a prolific voice in the data community through his blog, JamesSerra.com. James Serra discusses data lakehouses, which merge data lakes and data warehouses.

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Data Warehouse Data Lakes Data Science Big Data 52

Dataversity

OCTOBER 4, 2022

The rise of data lakes and adjacent patterns such as the data lakehouse has given data teams increased agility and the ability to leverage major amounts of data. Constantly evolving data privacy legislation and the impact of major cybersecurity breaches has led to the call for responsible data […].

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Data Lakes Data Governance Data Quality 94

Dataversity

NOVEMBER 10, 2022

There are several reasons why the notion of semantic layers has reached the forefront of today’s data management conversations. The analyst community is championing the data fabric tenet. The data mesh and data lake house architectures are gaining traction. Data lakes are widely deployed.

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Dataversity

MARCH 12, 2021

blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […].

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Data Lakes SQL Database Data Science 52

Dataversity

MARCH 6, 2023

Data lakes and semantic layers have been around for a long time – each living in their own walled gardens, tightly coupled to fairly narrow use cases. As data and analytics infrastructure migrates to the cloud, many are challenging how these foundational technology components fit in the modern data and analytics stack.

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Data Lakes Analytics Analytics Cloud Data 52

Dataversity

JULY 9, 2021

blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source DevOps on the cloud with protected internal legacy tools, SQL with NoSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […].

Azure 52

Azure Data Lakes SQL ML 52

Towards AI

MARCH 28, 2024

Building an Enterprise Data Lake with Snowflake Data Cloud & Azure using the SDLS Framework. By Richie Bachala This blog delves into the intricacies of building these critical data ingestion designs into Snowflake Data Cloud for enterprises. Think a friend would enjoy this too?

AI 84

AI AI Data Lakes Azure 84

Dataversity

MAY 7, 2021

blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source DevOps on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […].

Data Lakes 52

Data Lakes SQL Database Data Science 52

Dataversity

APRIL 23, 2021

Within the Data Management industry, it’s becoming clear that the old model of rounding up massive amounts of data, dumping it into a data lake, and building an API to extract needed information isn’t working. Click to learn more about author Brian Platz.

Database 90

Database Data Lakes Data Silos Data Governance 90

Dataversity

FEBRUARY 10, 2023

For many of these organizations, the path toward becoming more data-driven lies in the power of data lakehouses, which combine elements of data warehouse architecture with data lakes.

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Data Lakes Data Warehouse Data Quality Data Governance 52

Dataversity

FEBRUARY 2, 2022

blog series, we experiment with the most interesting blends of data and tools. In the “Will They Blend?”

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Data Lakes SQL Analytics Analytics 52

Dataversity

JANUARY 25, 2023

Over the past few years, enterprise data architectures have evolved significantly to accommodate the changing data requirements of modern businesses. Data warehouses were first introduced in the […] The post Are Data Warehouses Still Relevant?

Data Warehouse 52

Data Warehouse Data Lakes Cloud Computing Data Pipeline 52

AWS Machine Learning Blog

MARCH 1, 2023

Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored. Periodically, the new labeled data (to retrain the model) can be made available to flywheel by creating datasets. One for the data lake for Comprehend flywheel.

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Data Lakes AWS ML ML 68

ODSC - Open Data Science

AUGUST 15, 2023

Recently we’ve seen lots of posts about a variety of different file formats for data lakes. There’s Delta Lake, Hudi, Iceberg, and QBeast, to name a few. It can be tough to keep track of all these data lake formats — let alone figure out why (or if!) And I’m curious to see if you’ll agree.

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Dataversity

SEPTEMBER 12, 2022

The ways in which we store and manage data have grown exponentially over recent years – and continue to evolve into new paradigms. For much of IT history, though, enterprise data architecture has existed as monolithic, centralized “data lakes.” The post Data Mesh or Data Mess?

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Data Lakes Data Quality Data Governance Cloud Data 52

ODSC - Open Data Science

MAY 31, 2023

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

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Dataversity

MAY 19, 2023

In the early days of business analysis and underwriting, data was managed with simply a pen and paper and, of course, Excel spreadsheets. As technology has advanced, databases, warehouses, and data lakes have enabled information to be collected, stored, and managed electronically.

Data Lakes 52

Data Lakes Database Data Quality 52

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

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Data Lakes Data Warehouse Database ETL 52

phData

APRIL 29, 2024

In our previous blog, Top 5 Fivetran Connectors for Financial Services , we explored Fivetran’s capabilities that address the data integration needs of the finance industry. Now, let’s cover the healthcare industry, which also has a surging demand for data and analytics, along with the underlying processes to make it happen.

SQL 52

SQL Azure Data Warehouse Cloud Data 52

SAS Software

NOVEMBER 29, 2023

Azure Data Lake Storage (ADLS) Gen2のストレージアカウントの作成 3-2.ストレージアカウントのデータストレージコンテナの作成 Azure Data Lake Storage (ADLS) Gen2のストレージアカウントの作成 3-2.ストレージアカウントのデータストレージコンテナの作成 The post データ分析効率化の秘訣：SAS ViyaとAzure Synapseの高速データ転送方法の紹介 appeared first on SAS Blogs. Bulkload機能について 3.BULKLOAD機能を利用するためのAzure側で必要なサービスの作成 Azureアプリの設定 4.SAS

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Dataversity

DECEMBER 16, 2020

According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to the disparate data sources in legacy systems, new system deployments, and the creation of data lakes and data warehouses. Most organizations do not utilize the entirety of the data […].

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Data Warehouse Data Lakes Data Profiling Data Quality 69

Alation

FEBRUARY 13, 2020

A lot of people in our audience are looking at implementing data lakes or are in the middle of big data lake initiatives. I know in February of 2017 Munich Re launched their own innovative platform as a cornerstone for analytics that involved a big data lake and a data catalog.

Data Lakes 52

Data Lakes Analytics Analytics Data Engineering 52

IBM Journey to AI blog

APRIL 15, 2024

Or we create a data lake, which quickly degenerates to a data swamp. Build new digital capabilities with generative AI The post 4 ways generative AI addresses manufacturing challenges appeared first on IBM Blog. Or we keep adding applications, so our technical debt continues to increase.

AI 105

AI AI Data Lakes Analytics 105

Data Science Blog

MARCH 3, 2023

Übrigens nicht mehr so stark bei den Data Scientists, auch wenn richtig gute Mitarbeiter ebenfalls rar gesät sind, den größten Bedarf haben Unternehmen eher bei den Data Engineers. Das sind die Kollegen, die die Data Warehouses oder Data Lakes aufbauen und pflegen. appeared first on Data Science Blog.

Data Warehouse 147

Data Warehouse Data Lakes Data Engineering Data Engineer 147

The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

Alation

OCTOBER 21, 2021

Data Swamp vs Data Lake. When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. I’ll take the lake, thank you very much. Many organizations have built a data lake to solve their data storage, access, and utilization challenges.

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Data Lakes Data Governance Data Warehouse Business Intelligence 52

Data Science Blog

MARCH 14, 2023

Big Data Analytics erreicht die nötige Reife Der Begriff Big Data war schon immer etwas schwammig und wurde von vielen Unternehmen und Experten schnell auch im Kontext kleinerer Datenmengen verwendet. 2 Denn heute spielt die Definition darüber, was Big Data eigentlich genau ist, wirklich keine Rolle mehr.

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IBM Journey to AI blog

JANUARY 19, 2024

The solution, as discussed by McKinsey, is to create a data lake where all the collected data pools and relevant parties have access to aggregate information to make smarter decisions. Then CX and customer service professionals can use customer relationship management (CRM) tools to take actions on this data.

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Smart Data Collective

FEBRUARY 20, 2022

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. This blog post has demonstrated how AWS can greatly benefit your SaaS company, on multiple levels. Conclusions.

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