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Learn how Substack has utilized Datacoral’s fast data syncs, hard deletes, automatic schema change detection, and more to make their team more efficient.
Every company wants to deliver high-value data insights, but not every company is ready or able. Too often, they believe the marketing hype around point-and-click, no-code data connectors. Just set it and forget it, and all the hard work gets done — right? The hard truth is there are several critical steps that all data must go through in order for teams to get to what they want: high-value insights.  In many respects, centralizing the data is the easy part. There are more options than ever to quickly and easily build pipelines that ingest data from hundreds of sources
data infrastructure
Introduction Data connectors are a critical component of any company’s data infrastructure stack. They replicate the company’s data from different sources into a shared data warehouse or data lake, which then enables the data team to aggregate, combine, and explore all these datasets together. When a data connector is set up, it will start reading the data and the changes as they happen at the source, but will also trigger a “historical sync” for the table to fetch all the existing data, so that the data at the destination matches data at the source exactly.  Historical syncs are needed
data integration and change data capture
This article was originally posted on Towards Data Science here. Data integrations have been around for decades, but there has been a recent explosion of new, compelling data integration companies offering cloud-native, easy-to-configure connectors and quick access to high-value analytics. These offerings come from companies like Fivetran, Stitch Data, Matillion, and Airbyte. It can be difficult for any data team to know whether to stick to their existing data stack or to adopt these new tools. Though it’s great to see data integrations becoming more mainstream and new approaches emerging, we think there’s a growing gap being created by new
Change Data Capture with Google Analytics
Introduction Let’s pretend you work at a small identity protection startup and you’re getting acquired by a Fortune 100 corporation. You and 120 of your peers are waiting on huge acquisition checks, the champagne is on order, and there’s palpable, anxious excitement in the air. Morale is high and you’re all ready to join the big leagues. There are nervous smiles everywhere you look and you’re waiting to go on a hiring spree to start scaling. You’re feeling good… Until an email comes in from the acquiring corporation’s security and compliance group. You’re being asked some intense questions for
Datacoral AWS Startup Architecture 2020
Last year, we had shared that Datacoral had been selected as a finalist for the AWS Startup Architecture competition in the US. Today, we are excited to announce that Datacoral has been declared as the winner of this competition! Many organizations today are struggling to extract and consolidate data from many different data sources into a centralized warehouse. Datacoral provides data connectors for multiple sources (including change data capture connectors for databases) that allow business-critical data to be readily available for analytics for our customers. However, just moving data from source to destination isn’t enough. Data should remain secure,
Secure Data Pipelines in Your Cloud, Fully Managed
Introduction As businesses learn how to make up-to-the-minute, data-driven decisions, their data teams face increasing scope demands. The challenges these data teams face include getting access to a plethora of data sources; understanding the business value that resides in them; managing and moving large data sets; and finding the time to build meaningful dashboards and algorithms that drive business outcomes. Adding more data experts to the team may spread the load, but does not eliminate the myriad distractions from their core role: data analytics. Furthermore, data teams are often working with privileged user data — think patient data in
Various Data Connectors
Introduction Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times. Let’s take an example. Maybe you’re trying to understand how COVID-19
3 Layer Framework
Introduction How do you think about your data stack? If your job requires analyzing data, or supporting teams who analyze your data, you will likely find yourself at some point working on data pipelines and learning about their complexity as you go along. While data pipelines are light years ahead of where they were 20 years, the day-to-day challenges data teams face today are largely the same. How have we come so far, yet stayed where we are? We have seen tons of thoughtful work being done to promote the modern data stack. Andreessen Horowitz’s seminal thought piece on
Datacoral Newsletter
This post was originally an email which our CEO, Raghu Murthy, shared with Datacoral customers on Tuesday, January 19th, 2021. It is shared with light edits. This update started as a recap of 2020, where we shared our comments on what a strange year it had been. But that seems like old news now, compared to the eventful start to 2021. We hope with everything going on, you, your loved ones, and your teams are healthy and well. We are heartened and excited about everything the Datacoral data community has achieved in 2020 and have some critical product fixes
Poeye stopped AWS Outage
This post was originally an email which our CEO, Raghu Murthy, shared with Datacoral customers in response to the AWS outage that occurred on Wednesday, November 25th, 2020. It is shared with light edits. We hope that you and your loved ones had a relaxing holiday break. One that was not too heavily impacted by the AWS outage last week. In case you weren’t aware, AWS had a pretty significant outage of its services in the us-east-1 region on the day before the Thanksgiving break. You can read more about the outage from The Verge and from the Washington
Datacoral achieves SOC 2 Type 1 Compliance
Datacoral is a provider of end-to-end data infrastructure for our customers, from connectors that ingest data from many data sources into data warehouses to transformations that allow data scientists to extract insights from their data. We care deeply about our customer’s trust in us and have always been committed to maintaining data security and privacy. In the past, we have achieved HIPAA certification and are part of the Privacy Shield program. Today, Datacoral is excited to announce that we are now Service Organization Control (SOC) 2, Type 1 Compliant. We were able to easily meet all of the security
Marketo plus Datacoral
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
Datacoral Newsletter
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
Datacoral performs Historical Syncs for Database CDC Connectors
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
Synchronous vs Asynchronous Invocations
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
Redshift Data API Flowchart
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
Datacoral Data Capture Flowchart
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
Rollbar plus Datacoral
We are excited to share that our customers who use Rollbar for error monitoring in their software applications can now use our Rollbar connector to better analyze their current and historical software deployment errors and alerts across different environments. Rollbar enables software engineering teams to deploy better software, faster (we use Rollbar internally as well!). This is done by providing real-time alerting and insights into errors as they happen, so that the engineering team is empowered to fix issues before customers report them. Rollbar makes it easy to instrument applications across many different languages and platforms (Python, Javascript, iOS,
How to Audit your AWS Account
At Datacoral, we spend a lot time thinking about security and building our product in such a way that our customers have complete control over their data and credentials inside of their Amazon Web Services (AWS) accounts. This has led to our unique architecture that allows for secure data ingestion from all kinds of datasources into the data warehouse of choice for our customers. Datacoral’s software deployment model involves installing our software in our customer’s VPC. Datacoral doesn’t have access to any customer data and our customers’ DevOps teams can have full control over all parts of their data
Drift plus Datacoral
We are excited to share that our customers who use Drift as their conversational marketing platform can now better understand how their prospective customers are communicating with them by using our Drift connector. Drift helps their customers convert their digital traffic into revenue through personalized targeting and engagement with intelligent chatbots. Drift chatbots qualify leads who visit their customers’ websites, automatically book meetings for sales teams, answer questions using a knowledge base and help up-level modern B2B Marketing and Sales teams. However, these Chatbots require monitoring and improvement to better respond to ever-changing behavior patterns of software buyers today.
Datacoral selected as finalist for AWS Startup Architecture of the Year
We are proud that AWS has selected Datacoral as one of the seven finalists from San Francisco for the AWS Startup Architecture of the Year Program! Datacoral’s Data Integration Platform for Analytics has a unique combination of serverless architecture with a cloud on-prem software delivery model. This combination provides complete data security, incredible scalability and convenience for our customers. This Platform has allowed to replicate terabytes of data from diverse kinds of data sources (databases, APIs, file systems and event streams) into different cloud warehouses without our customers having to write a single line of code or worrying about
Datacoral Connector: JDBC
Datacoral is excited to announce the release of our JDBC connector for ingesting data from any database into any data warehouse through the standardized JDBC interface. While Datacoral already supported ingesting data from multiple databases (such as PostgreSQL and MySQL), we wanted to open up our data integration platform for all databases and SaaS tools based sources that provide data access through JDBC. So, with our JDBC connector, our customers can easily replicate data from databases like Oracle and Microsoft SQL Server and services like NetSuite and SAP By Design that offer JDBC drivers all the while ensuring that
AWS Advanced Technology Partner
We have been building Redshift-optimized data pipelines so that our customers get the most out of their Redshift clusters both in terms of cost and performance. We are proud to have achieved our Redshift Ready Designation! Original press release here: https://www.prnewswire.com/news-releases/datacoral-achieves-amazon-redshift-ready-designation-301103866.html SAN FRANCISCO, Aug. 3, 2020 /PRNewswire/ — Datacoral, a secure data integration platform, announced today that it has 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. Achieving the Amazon Redshift Ready designation differentiates Datacoral as an AWS Partner
Datacoral Connector: Mux
Datacoral is excited to announce that our customers can better understand their video streaming performance by integrating video analytics from Mux Data using our Mux connector. Mux Data allows companies to understand video performance and the experience of users as they’re interacting with videos. In today’s world, having a granular understanding of every user’s experience is critical for the success of a product. Mux does an incredible job making it easy to gather the required data using just a few lines of code and provides an intuitive dashboard for navigating this data. Working with video data can be challenging
Datacoral Newsletter
\At Datacoral, we have had a very interesting transition to an all-remote team during the craziness that has been the COVID Quarter Q2 2020. We even moved our weekly yoga sessions to Zoom! We did a 24 hour remote hackathon where folks in the engineering team worked on several cool ideas to improve our developer velocity as we continue to work remotely. Latest news We now offer AWS credits for trials to a fully-featured secure free trial! We achieved Amazon Linux-2 Ready Designation this quarter. Our founder’s paper on Hive won the IEEE ICDE 2020 Influential Paper Award for
Jyve Leveraged Datacoral’s Batch Compute to Better Understand its Marketplace
Jyve: Company Background Jyve serves the Grocery and CPG industry by connecting them with a pool of skilled labor professionals, called Jyvers, who are trained to complete jobs like restocking shelves and auditing goods for expiry. As a gig-economy platform, Jyve understands that success lies in making both its partners and Jyvers happy and that data is an invaluable tool to gain insight into its marketplace dynamics. Focusing on the needs of its partners and Jyvers represents a mission that is unique to Jyve, so when Jyve’s tech team was beginning to grow its Data team it made sense
Datacoral’s Event-Driven Orchestration Framework Now Available to Customers
Introduction Datacoral provides an end-to-end data engineering platform to extract, load, transform and serve business data. In short, we help data scientists create and manage data pipelines with only a few clicks. As part of our installation process, we deploy a set of micro-services within our customers’ VPC to help them with their data engineering workloads. These services could be fetching data from a database like Postgres and loading data into a warehouse like Redshift, Snowflake or Athena. Internally, our services communicate via events, which we call “data events”. In this blog post, we will first explore what these
Datacoral offers Free and Secure Trials on AWS
Datacoral’s end-to-end data engineering platform allows customers to extract, load, transform and serve their business data, while ensuring that the data is always secure within the customer’s VPC. Datacoral’s stance on trials is to run them within our customer’s environments. This differentiates us from the typical SaaS vendors because in trials with us, customers can: run their production data using our software run all of their production traffic/workloads against their production data for scale testing This means that customers can try out Datacoral while ingesting their data securely. See what our current customers have to say about us! We
Datacoral Achieves Amazon Linux 2 Ready Designation
We have worked with AWS from the very beginning. We are proud of the team for staying on top of the latest and greatest AWS offerings! Original press release here: https://www.prnewswire.com/news-releases/datacoral-achieves-amazon-linux-2-ready-designation-301043899.html SAN FRANCISCO, April 21, 2020 /PRNewswire/ — Datacoral, a Data Engineering Company, announced today that it has achieved the Amazon Linux 2 Ready designation, part of the Amazon Web Services (AWS) Service Ready Program. This designation recognizes that Datacoral’s Data Engineering Platform has been validated to run on and support Amazon Linux 2. Achieving the Amazon Linux 2 Ready designation differentiates Datacoral as an AWS Partner Network (APN)
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,
AWS Summit New York
Datacoral is headed to New York City on Thursday, July 11 as a Silver sponsor of AWS Summit at the Jacob Javits Center. The event is free and runs from 7 AM to 6:30 PM. We will be showing off our AWS-based Data Infrastructure as a Service (DIaaS) for data engineers, data scientists, Redshift administrators and BI analysts. Datacoral is a complete, end-to-end data pipeline service that runs securely in your VPC, connects to your cloud data, organizes and orchestrates it in Redshift, and allows users, applications and original sources to harness the results. Data is delivered as materialized
How much time do you spend maintaining your data pipeline? How much end user value does that provide? Raghu Murthy founded DataCoral as a way to abstract the low level details of ETL so that you can focus on the actual problem that you are trying to solve. In this episode he explains his motivation for building the DataCoral platform, how it is leveraging serverless computing, the challenges of delivering software as a service to customer environments, and the architecture that he has designed to make batch data management easier to work with. This was a fascinating conversation with
Datacoral
Every day we produce 2.5 quintillion bytes of data. In a bid to make sense of all this data, businesses have begun to employ and train data scientists and machine learning engineers. These job categories are so hot that a report by LinkedIn found that 6.8X more people list their jobs as Data Scientists and 9.8X more as Machine Learning Engineer today than they did five years ago.
Introducing Datacoral: A Secure, Scalable Data Infrastructure
Today, I could not be more excited to publicly launch Datacoral and announce our $10M Series A, led by Sudip Chakrabarti at Madrona Venture Group with participation from Social Capital and other investors. At Datacoral, we are taking a fresh look at how data-driven companies can dramatically reduce time spent managing data infrastructure — and instead focus on driving more business value from that data. Today, companies of all sizes want to be data-driven, which means successfully using the data they own to make their products or services better. But building infrastructure can be one of the biggest barriers to companies
ستستمتع معظم النساء بمشاهدة xnxx ، لكن في بعض الأحيان يواجه ذلك مشاكل: العديد من الأفلام الإباحية مصممة من قبل الرجال فقط ، وخاصة للرجال. غالبًا ما يُجبر معظم نجوم البورنو على التصرف بطريقة معينة ، وحتى عندما يكونون جذابين بشكل طبيعي ، فهم عادةً غير واثقين بما يكفي لعرض ذلك في xnxx الخاص بهم. لنواجه الأمر؛ مشاهدة الإباحية المثيرة ليست بالضبط ما تتحدث عنه الثقة. ولكن باستخدام هذه النصائح ، ستتمكن من تحويل مشاهدك الإباحية المفضلة إلى خيال جنسي لأحلامك. ابدأ ببعض التلميحات الأكثر دقة في جميع أنحاء المواقع الإباحية التي يمكن للمرأة الارتباط بها. على سبيل المثال
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