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SaaS (software-as-a-service) products are ubiquitous in the modern enterprise. SaaS is great for cost control and ease of management for both the vendor and the customer, but those advantages come with trade-offs around data security models, primarily due to commingling of data in the multi-tenant architectures of SaaS vendors. In this blog, we claim that you can have your cake (cost/ease of management) and eat it (security) too! We explore changing the software delivery model of SaaS products to solve for security while maintaining the traditional advantages of SaaS products by leveraging serverless offerings from cloud vendors (AWS, Azure,
What does it mean to ingest data securely into your cloud?
One of the common challenges companies face is that they want to centralize their data present in multiple systems like databases, Sales tools, HR systems, Finance systems, Marketing tools, file systems, and even event streams into their data warehouse for analysis. There are different levels of data privacy/security concerns for different kinds of data in each of these data sources. Several of these data sources, like production databases, Finance systems, and HR systems have really sensitive data like personally identifiable information (PII), personal health information (PHI), salary information, financial transactions of either employees or customers of those companies. And
Amazon Redshift update in Andy Jassy’s keynote at AWS re:Invent 2019
Introduction Datacoral supports several database engines as part of its transformation technology. Using existing databases allows us to leverage the significant R&D effort that specialized vendors have invested in functionality, performance, and scalability. As database technology continues to evolve, it is important for us and our customers to understand the new capabilities that are being developed and take advantage of them. Datacoral supports Amazon Redshift and was in fact show cased as a Redshift Ready Partner in the Global Partner Keynote at the AWS re:Invent 2019 event. Redshift is changing at a rate of hundreds of new features and
September Review Datacoral Infrastructure
September 2019 September was a big month for marketing data infrastructure at Datacoral, especially in the context of our partnership with Amazon Web Services.  At the beginning of the month we conducted and published our first webinar recording:  The top 5 Requirements for AWS-Native data pipelines  This event covered how Datacoral goes beyond popular cloud-based ETL and ELT products to support a cost effective, scalable and compelling data infrastructure platform within AWS using native AWS services.  Beyond supporting AWS-best practice ELT centered around S3, Redshift and Athena, the five additional requirements are:  Serverless architecture using AWS Lambda Functions as
Building Serverless Data Pipelines on Amazon Redshift By Writing SQL with Datacoral
This blog post originally appeared in the AWS Partner Network (APN) Blog. Amazon Redshift is a powerful yet affordable data warehouse, and while getting data out of Redshift is easy, getting data into and around Redshift can pose problems as the warehouse grows. Data ingestion issues start to emerge when organizations outgrow the COPY command that imports CSV files from Amazon Simple Storage Service (Amazon S3) buckets. And, of course, these issues take shape at the worst possible time—when the warehouse becomes popular with analysts and data scientists as they increase demand for more data, sources, and targets. When
How Many Team Members is Ideal
And what is the ROI if I don’t have to hire so many team members? I’ve been at Datacoral for two months.  In that time, I’ve met or corresponded with most of our customers. What impresses me is how they describe the value that our data-infrastructure-as-a-service brings them. More than one says that we have saved them from needing to hire a team of engineers to build out and manage their data infrastructure. Okay, that sounds like pretty big value, but it’s abstract value, because I can’t immediately assess what ‘team’ means in terms of membership size, responsibility, roles,

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