AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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AWS Big Data

JUNE 10, 2024

In this blog post, we dive into different data aspects and how Cloudinary breaks the two concerns of vendor locking and cost efficient data analytics by using Apache Iceberg, Amazon Simple Storage Service (Amazon S3 ), Amazon Athena , Amazon EMR , and AWS Glue. withRegion("us-east-1").build() withQueueUrl(queueUrl).withMaxNumberOfMessages(10)).getMessages.asScala

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AWS Big Data

OCTOBER 30, 2024

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze your data using standard SQL and your existing business intelligence (BI) tools. Data ingestion is the process of getting data to Amazon Redshift.

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DataKitchen

NOVEMBER 4, 2021

Cloud computing has made it much easier to integrate data sets, but that’s only the beginning. Creating a data lake has become much easier, but that’s only ten percent of the job of delivering analytics to users. It often takes months to progress from a data lake to the final delivery of insights.

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AWS Big Data

MAY 30, 2024

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Solution overview Amazon Redshift is an industry-leading cloud data warehouse.

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Data Warehouse Data Lake Cost-Benefit Structured Data 106

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. With pay-as-you-go pricing, platforms that deliver high-performance benefit users not only through faster results but also through direct cost savings.

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AWS Big Data

MARCH 2, 2023

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. AWS Glue 3.0 The following diagram illustrates the solution architecture.

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AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

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AWS Big Data

JUNE 21, 2023

In traditional databases, we would model such applications using a normalized data model (entity-relation diagram). A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. These types of queries are suited for a data warehouse.

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AWS Big Data

APRIL 3, 2024

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

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AWS Big Data

JANUARY 15, 2025

cycle_end"', "sagemakedatalakeenvironment_sub_db", ctas_approach=False) A similar approach is used to connect to shared data from Amazon Redshift, which is also shared using Amazon DataZone. AWS Database Migration Service (AWS DMS) is used to securely transfer the relevant data to a central Amazon Redshift cluster.

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AWS Big Data

DECEMBER 4, 2024

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

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AWS Big Data

MAY 20, 2024

Whether you are new to Apache Iceberg on AWS or already running production workloads on AWS, this comprehensive technical guide offers detailed guidance on foundational concepts to advanced optimizations to build your transactional data lake with Apache Iceberg on AWS. He can be reached via LinkedIn. He can be reached via LinkedIn.

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AWS Big Data

DECEMBER 12, 2024

Satori enables both just-in-time and self-service access to data. Solution overview Satori creates a transparent layer providing visibility and control capabilities that is deployed in front of your existing Redshift data warehouse. This increases the time-to-value of data and drives innovative decision-making.

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DataKitchen

DECEMBER 7, 2021

This book is not available until January 2022, but considering all the hype around the data mesh, we expect it to be a best seller. In the book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today’s organizations.

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AWS Big Data

JUNE 23, 2023

Events and many other security data types are stored in Imperva’s Threat Research Multi-Region data lake. Imperva harnesses data to improve their business outcomes. As part of their solution, they are using Amazon QuickSight to unlock insights from their data.

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CIO Business Intelligence

NOVEMBER 25, 2022

Today, more than 90% of its applications run in the cloud, with most of its data is housed and analyzed in a homegrown enterprise data warehouse. Like many CIOs, Carhartt’s top digital leader is aware that data is the key to making advanced technologies work. Today, we backflush our data lake through our data warehouse.

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DataKitchen

AUGUST 27, 2021

It implemented hundreds of schema and data set changes per week without introducing errors. Arguably the most agile and effective data analytics capability in the pharmaceutical industry was accomplished cost-effectively, with a data engineering team of seven and another 10-12 data analysts.

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AWS Big Data

NOVEMBER 22, 2024

Data practitioners need to upgrade to the latest Spark releases to benefit from performance improvements, new features, bug fixes, and security enhancements. This process often turns into year-long projects that cost millions of dollars and consume tens of thousands of engineering hours. job to AWS Glue 4.0.

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AWS Big Data

JUNE 25, 2024

This post is co-authored by Vijay Gopalakrishnan, Director of Product, Salesforce Data Cloud. In today’s data-driven business landscape, organizations collect a wealth of data across various touch points and unify it in a central data warehouse or a data lake to deliver business insights.

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CIO Business Intelligence

JUNE 23, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

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Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

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IBM Big Data Hub

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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Cloudera

APRIL 3, 2023

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. It allows us to independently upgrade the Virtual Warehouses and Database Catalogs.

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Sisense

NOVEMBER 12, 2020

Data warehouse vs. databases Traditional vs. Cloud Explained Cloud data warehouses in your data stack A data-driven future powered by the cloud. We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Data warehouse vs. databases.

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AWS Big Data

NOVEMBER 8, 2023

Because Gilead is expanding into biologics and large molecule therapies, and has an ambitious goal of launching 10 innovative therapies by 2030, there is heavy emphasis on using data with AI and machine learning (ML) to accelerate the drug discovery pipeline. Loading data is a key process for any analytical system, including Amazon Redshift.

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Data Lake Data Warehouse Cost-Benefit Optimization 93

AWS Big Data

AUGUST 21, 2024

The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. This post also includes example SQLs, which you can run on your own Redshift Serverless data warehouse to experience the benefits of this feature.

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Sisense

JULY 8, 2020

Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. In recent years we’ve seen data become vastly more available to businesses. This has allowed companies to become more and more data driven in all areas of their business.

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IBM Big Data Hub

APRIL 24, 2023

Data is reported from one central repository, enabling management to draw more meaningful business insights and make faster, better decisions. By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement.

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Cloudera

MAY 28, 2024

Data architecture is what defines the structures and systems within an organization responsible for collecting, storing, and accessing data, along with the policies and processes that dictate how data is governed. When we talk about modern data architecture, there are several unique benefits to this kind of approach.

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AWS Big Data

FEBRUARY 12, 2024

About Redshift and some relevant features for the use case Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that offers simple operations and high performance. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

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BizAcuity

NOVEMBER 22, 2022

This typically requires a data warehouse for analytics needs that is able to ingest and handle real time data of huge volumes. Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts allowing secure data sharing across the organization.

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AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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AWS Big Data

AUGUST 1, 2024

Large-scale data warehouse migration to the cloud is a complex and challenging endeavor that many organizations undertake to modernize their data infrastructure, enhance data management capabilities, and unlock new business opportunities. This makes sure the new data platform can meet current and future business goals.

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AWS Big Data

JUNE 26, 2024

New feature: Custom AWS service blueprints Previously, Amazon DataZone provided default blueprints that created AWS resources required for data lake, data warehouse, and machine learning use cases. You can build projects and subscribe to both unstructured and structured data assets within the Amazon DataZone portal.

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AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. These upstream data sources constitute the data producer components.

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CIO Business Intelligence

MAY 24, 2022

The second will focus on the growth in volume and type of data required to be stored and managed, and the ways in which value can be extracted from data. The third will examine the challenges of realising that value, the attributes of a successful data-driven organisation, and the benefits that can be gained.

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