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

Data Warehouse Data Lakes Azure AWS 147

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.

Data Lakes 59

Data Lakes Data Warehouse Data Quality Data Governance 59

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.

Data Lakes 52

Data Lakes Machine Learning Machine Learning Data Quality 52

Tableau

JULY 28, 2021

Our technology partner Dremio offers a next-generation data lake engine to securely query a customer’s cloud data lake storage directly. An increasing number of customers have adopted data lakes as the foundation of their data platform. Get started using the native Dremio connector today.

Data Lakes 52

Data Lakes Tableau Cloud Data Cloud Computing 52

IBM Journey to AI blog

FEBRUARY 2, 2023

These developments have accelerated the adoption of hybrid-cloud data warehousing; industry analysts estimate that almost 50% 2 of enterprise data has been moved to the cloud. What is holding back the other 50% of datasets on-premises? However, a more detailed analysis is needed to make an informed decision.

Data Warehouse 58

Data Warehouse Cloud Data Analytics Analytics 58

phData

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 Lakes 52

Data Lakes Data Warehouse Cloud Data AWS 52

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 101

Tableau Data Lakes Data Warehouse SQL 101

Data Science 101

NOVEMBER 7, 2019

Microsoft just held one of its largest conferences of the year, and a few major announcements were made which pertain to the cloud data science world. Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Here they are in my order of importance (based upon my opinion).

Data Science 106

Data Science Azure SQL Machine Learning 106

Dataversity

AUGUST 5, 2021

According to Gartner, data fabric is an architecture and set of data services that provides consistent functionality across a variety of environments, from on-premises to the cloud. Data fabric simplifies and integrates on-premises and cloud Data Management by accelerating digital transformation.

Cloud Data 98

Cloud Data Data Silos Data Lakes Data Warehouse 98

Data Science Dojo

SEPTEMBER 11, 2024

With this full-fledged solution, you don’t have to spend all your time and effort combining different services or duplicating data. Overview of One Lake Fabric features a lake-centric architecture, with a central repository known as OneLake.

Power BI 195

Power BI Data Pipeline Data Warehouse Data Engineering 195

AWS Machine Learning Blog

NOVEMBER 1, 2024

Instead, a core component of decentralized clinical trials is a secure, scalable data infrastructure with strong data analytics capabilities. Amazon Redshift is a fully managed cloud data warehouse that trial scientists can use to perform analytics.

AWS 121

AWS Data Lakes Machine Learning Machine Learning 121

KDnuggets

DECEMBER 14, 2021

We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

Data Science 399

Data Science Machine Learning Machine Learning Analytics 399

Dataconomy

SEPTEMBER 4, 2023

Define data ownership, access controls, and data management processes to maintain the integrity and confidentiality of your data. Data integration: Integrate data from various sources into a centralized cloud data warehouse or data lake.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

Smart Data Collective

OCTOBER 27, 2021

Google BigQuery is a serverless and cost-effective multi-cloud data warehouse. It is easy to integrate with any existing data pipelines, and it can also stream data from the most popular message buses such as Amazon Kinesis and Kafka. It can also batch load files from data lakes such as Amazon S3 and HDFS.

Analytics 125

Analytics Analytics Data Warehouse Business Intelligence 125

Analytics Vidhya

MARCH 5, 2023

Introduction Microsoft Azure HDInsight(or Microsoft HDFS) is a cloud-based Hadoop Distributed File System version. A distributed file system runs on commodity hardware and manages massive data collections. It is a fully managed cloud-based environment for analyzing and processing enormous volumes of data.

Hadoop 319

Hadoop Azure Analytics Analytics 319

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.

Data Lakes 52

Data Lakes Analytics Analytics Cloud Data 52

Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases.

ML 153

ML ML AWS AI 153

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?

Data Lakes 52

Data Lakes Data Quality Data Governance Cloud Data 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.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147

phData

NOVEMBER 8, 2024

Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data. Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. FAQs What is a Data Lakehouse?

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

phData

APRIL 29, 2024

Fivetran enables healthcare organizations to ingest data securely and effectively from a variety of sources into their target destinations, such as Snowflake or other cloud data platforms, for further analytics or curation for sharing data with external providers or customers.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance.

Data Governance 69

Data Governance Database Cloud Data Data Lakes 69

Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

ML 123

ML ML AWS Data Warehouse 123

Smart Data Collective

OCTOBER 26, 2019

While there is more of a push to use cloud data for off-site backup , this method comes with its own caveats. In the event of a network shutdown or failure, it may take much longer to restore functionality (and therefore connection) to a cloud-hosted off-site backup. Big Data Storage Concerns.

Big Data 48

Big Data Big Data Data Lakes Cloud Data 48

Alation

MARCH 1, 2022

Lineage helps them identify the source of bad data to fix the problem fast. Manual lineage will give ARC a fuller picture of how data was created between AWS S3 data lake, Snowflake cloud data warehouse and Tableau (and how it can be fixed). Time is money,” said Leonard Kwok, Senior Data Analyst, ARC.

Data Warehouse 52

Data Warehouse Data Lakes Cloud Data Database 52

phData

AUGUST 9, 2023

Open source big data tools like Hadoop were experimented with – these could land data into a repository first before transformation. Thus, the early data lakes began following more of the EL-style flow. Snowflake was optimized for the cloud, separating storage and computing.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

IBM Journey to AI blog

MAY 9, 2023

A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1] Savings may vary depending on configurations, workloads and vendors.

AI 110

AI AI Data Warehouse Machine Learning 110

AWS Machine Learning Blog

SEPTEMBER 5, 2023

We have over 50 TB of historical equipment data and expect this data to grow quickly as more HVAC units are connected to the cloud. Data processing and model inference need to scale as our data grows. Our next step is to integrate these insights into the upcoming release of Carrier’s Connected Dealer Portal.

AWS 129

AWS ML ML Machine Learning 129

Alation

DECEMBER 14, 2021

Compliance in the Cloud ( GDPR, CCPA ) is still in in its infancy and tough to navigate, with people wondering: How do you manage policies in the cloud? How do you provide access and connect the right people to the right data? AWS has created a way to manage policies and access, but this is only for data lake formation.

AWS 52

AWS Data Lakes Data Warehouse Machine Learning 52

ODSC - Open Data Science

JULY 11, 2023

Co-location data centers: These are data centers that are owned and operated by third-party providers and are used to house the IT equipment of multiple organizations. What are the similarities and differences between data centers, data lake houses, and data lakes? Not a cloud computer?

Data Lakes 40

Data Lakes AI AI Cloud Computing 40

Precisely

JANUARY 19, 2023

Today, mainframes deliver scalability and global access, and they’re still a key element of the infrastructure that makes private clouds possible at many organizations. In 2023, expect to see broader adoption of streaming data pipelines that bring mainframe data to the cloud, offering a powerful tool for “modernizing in place.”

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Alation

JUNE 21, 2022

Why start with a data source and build a visualization, if you can just find a visualization that already exists, complete with metadata about it? Data scientists went beyond database tables to data lakes and cloud data stores. Data scientists want to catalog not just information sources, but models.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Alation

JUNE 9, 2021

We have an explosion, not only in the raw amount of data, but in the types of database systems for storing it ( db-engines.com ranks over 340) and architectures for managing it (from operational datastores to data lakes to cloud data warehouses). Organizations are drowning in a deluge of data.

Data Governance 59

Data Governance Database Cloud Data Machine Learning 59

Precisely

JANUARY 18, 2024

And, they’re still a key element of the infrastructure that makes private clouds possible at many organizations. Instead of performing major surgery on their critical business systems, enterprises are opting for real-time data integration built around inherently reliable and scalable change data capture (CDC) technology.

AWS 52

AWS Artificial Intelligence Artificial Intelligence Cloud Computing 52

Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

Data Governance 59

Data Governance ML ML Cloud Data 59

Alation

MARCH 29, 2021

Alation helps connects to any source Alation helps connect to virtually any data source through pre-built connectors. Alation crawls and indexes data assets stored across disparate repositories, including cloud data lakes, databases, Hadoop files, and data visualization tools.

Machine Learning 59

Machine Learning Machine Learning Data Lakes Hadoop 59