Learn how Substack has utilized Datacoral's fast data syncs, hard deletes, automatic schema change detection, and more to make their team more efficient.
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- November 10, 2020
We are excited to announce that our customers who use Marketo for their marketing automation workflows can now use Datacoral’s Marketo connector to replicate all of their marketing data into their data warehouse of choice (Snowflake, Redshift or Athena). Marketo enables digital marketing teams to be successful in a variety of ways, whether by helping personalize content for the right audiences, growing customer relationships or getting a handle on account-based marketing. Marketo’s solutions provides all the capabilities for a marketing team to attract and nurture prospects and help the team collaborate with our functions in an organization. While Marketo
- October 14, 2020
Hope you are finding your zen in the Zoom way of life! Here at Datacoral, we have settled down into a cadence that allows us to move quickly and provide improved capabilities and services to our customers. Check out the latest news and product updates below Latest news We achieved the Amazon Redshift Ready designation, part of the Amazon Web Services (AWS) Service Ready Program. This designation recognizes that Datacoral has demonstrated successful integration with Amazon Redshift. We are proud that AWS has selected Datacoral as one of the seven finalists from San Francisco for the AWS Startup Architecture
- October 6, 2020
In a previous post, we looked at why replicating data using the change logs (Change Data Capture, or CDC) is important and what are the common challenges in this approach. In this post, we are going to take a deeper look at managing historical syncs while replicating production databases to a data warehouse, and is the critical first step in this process. Path followed by the bold blue arrows shows the historical sync performed by Datacoral CDC connectors Why historical syncs? The aim of any connector is to make sure that data at the source is identical to the
- September 28, 2020
At Datacoral, we are heavy users of AWS Lambda and other AWS Serverless services because of the power, flexibility and security that we can provide our users by building our architecture on top of these. The benefits of these serverless services include: No resource provisioning required; services scale up and down seamlessly based on demand Fault tolerance and retry mechanisms are handled by the services themselves Services end up being cheaper for most real-world workloads (since machines don’t have to be provisioned for highest levels of traffic) Lightweight and fast deployments Very tight integration with other cloud services for
- September 16, 2020
Datacoral integrates data from databases, APIs, events and files into Amazon Redshift while providing guarantees on data freshness and data accuracy to ensure meaningful analytics. Using the Redshift API, we are able to create a completely event-driven and serverless platform that makes data integration and loading easier for our mutual customers. Redshift Blog about the Data API (Sep 16 2020) The Data API simplifies access to Amazon Redshift by eliminating the need for configuring drivers and managing database connections. Instead, you can run SQL commands to an Amazon Redshift cluster by simply calling a secured API endpoint provided by
- September 15, 2020
Change Data Capture (CDC) is a common pattern for replicating data from databases to data warehouses by syncing the database change logs rather than fetching data from tables. Many data engineering teams we have spoken to (many of whom are our customers now) have mentioned the complexity of running a CDC system at scale. In this post, we will go over why CDC is important, what CDC for common production databases looks like and common challenges with using CDC. Why is CDC important? Many data-driven data companies today replicate data from different data sources into a data warehouse for
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