AWS Elastic Beanstalk is an easy way to deploy web applications and services. Just upload your code, and it handles the details like server provisioning, load-balancing, scaling, etc. In this article, Samson Omojola shows us how to deploy our Laravel apps to EBS.

Google Cloud has a genuine passion for solving technology problems that make a difference for our customers. With the release of our BigQuery Connector for SAP, we're taking a another big step towards solving a major challenge for SAP customers with a quick, easy, and inexpensive way to integrate SAP data with BigQuery, our serverless, highly scalable, and cost-effective multi cloud data warehouse designed for business agility.

From the modern application platform perspective, products should allow architects and DevOps teams to support dynamic topologies. That means a multi-platform capability is required but not sufficient. In fact, for several reasons, companies are looking for hybrid deployments to run their applications on several platforms simultaneously. Moreover, the topology should support and adjust for new and continuous architecture changes.

As I meet with our customers, there are always a range of discussions regarding the use of the cloud for financial services data and analytics. Customers vary widely on the topic of public cloud – what data sources, what use cases are right for public cloud deployments – beyond sandbox, experimentation efforts. Private cloud continues to gain traction with firms realizing the benefits of greater flexibility and dynamic scalability.

Azure Synapse Analytics, still commonly known as Azure Data Warehouse, is Microsoft's cloud data warehouse that processes relational and non-relational data for analytics. As one of the most popular data warehousing tools, Azure lets you generate real-time insights into almost every aspect of your business, from sales to customer service. But how do you get data to Azure in the first place? That's where an Extract, Transform, and Load (ETL) tool proves useful.

According to 451 Research, 96% of enterprises are actively pursuing a hybrid IT strategy. Modern, real-time businesses require accelerated cycles of innovation that are expensive and difficult to maintain with legacy data platforms. Cloud technologies and respective service providers have evolved solutions to address these challenges.

The Snowflake Data Cloud is a powerful place to work with data because we have made it easy to do difficult things with data, such as breaking down data silos, safely sharing complex data sets, and querying massive amounts of data. As customers move to the Data Cloud, their needs and timelines vary—our goal is to meet every customer where they are on their Data Cloud journey.

As the business world increasingly becomes dependent on technology, the way in which companies handle and store their data becomes even more important. Therefore, finding a safe and secure place to store company data is becoming a necessity in the digital age. One robust cloud data warehouse that has been helping many companies safely store their data is known as Amazon Redshift.

Amazon Web Services (AWS) has millions of customers, making it one of the most popular cloud platforms in the world. With over 200 services from data centers globally — compute, storage, databases, machine learning, artificial intelligence, data lakes, analytics, Internet of Things, etc. — AWS makes it easier for businesses to move applications to the cloud and streamline workflows. Still, many gaps exist in native AWS tools, which can limit your company’s AWS capabilities.

As anyone running modern data applications in the cloud knows, costs can mushroom out of control very quickly and easily. Getting these costs under control is really all about not spending more than you have to. Unfortunately, the common approach to managing these expenses—which looks at things only at an aggregated infrastructure level—helps control only about 5% of your cloud spend.

In this series of blog posts, we will showcase an end-to-end hybrid cloud ML workflow using the Iguazio MLOps Platform & Feature Store combined with Azure ML. This blog will be more of an overview of the solution and the types of problems it solves, while the next parts will be a technical deep dive into each step of the process.

Choosing the right data warehouse is a critical component of your general data and analytic business needs. One of the biggest questions that businesses ask when choosing their data warehouse providers is this: Should you use Snowflake, Amazon RedShift, or Google's BigQuery data warehouse for your business needs? We've already covered Amazon RedShift vs. Snowflake and Google BigQuery vs. Snowflake, but what about Amazon RedShift vs. Google BigQuery?

When it comes to cloud data integration there are many benefits. However, although there are many benefits of cloud data integration for your company, it also comes with its own set of challenges. In the business world of today, cloud technologies have become increasingly more important in the evolving business and IT landscapes. Cloud technologies are used more and more because of the convenience, performance and cost-effectiveness they provide to companies compared to on-premises solutions.

In 2021, many organizations on a digital transformation journey sought cloud-native data management and analytics solutions that could facilitate search and analytics on their data in the cloud. Many of them are already running on the AWS cloud, so naturally, they turned to the AWS Partner Network to find technologies that could easily plug into their cloud stack, solve tactical pains, and deliver value quickly.