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While it’s great that you offer a Forwarding Agent for sources/destinations that have no direct public IP, I’m having a hard time finding any information as to whether or what sort of encryption in transit is used for the data conveyed by the Agent. And before I go digging into testing what it appears to do, I thought I’d ask here: Does Striim Forwarding Agent use any encryption for any of its communication?Context here is that it was suggested to me that Striim might be useful in migrating from an antiquated MySQL ring replication cluster to a modern MySQL master-master cluster. Neither source nor destination cluster has, nor will have, a public IP address, and will not have any direct access available to to the greater internet.
This article was originally posted on May 17, 2022 Today, we launched the availability of Striim Cloud on Google Cloud, the fastest way for you to deliver real-time data and insights to power business intelligence and decision-making to meet the needs of the digital economy. Striim Cloud is the industry’s first and only fully-managed software-as-a-service (SaaS) platform for real-time data streaming and integration. With a few clicks, you can easily build real-time data pipelines to stream trillions of events every day, backed by enterprise-grade operational, security, and management features. Striim Cloud on Google Cloud delivers multiple capabilities for digital transformation to the enterprise: Striim Cloud delivers instant access to what’s happening right now across multiple applications Striim Cloud delivers the simplicity, elasticity, and scalability of the cloud Striim Cloud super-charges enterprise innovation To read more about Striim Cloud on Google C
Web UIThe web UI has been substantially redesigned, including improved Home and Files pages and new pages for managing property sets, property variables, and vaults. For more information, see the Web UI Guide.Application development You can create and manage property sets, property variables, and vaults in the web UI. Schema evolution supports TRUNCATE TABLE when the source is OJet (see Handling schema evolution). Sources and targets Database Reader can read from multiple MariaDB or MySQL databases and supports Oracle VARRAY. JMS Reader supports Transaction Policy with IBM MQ. Salesforce Reader will, in all modes, read objects without a CreatedDate field. (Previously this field was required in Incremental mode.) ServiceNow Reader reads ServiceNow tables. BigQuery Writer supports the Storage Write API (see BigQuery > Documentation > Guides > Batch load and stream data with BigQuery Storage Write API). Cosmos DB Writer can process inserts and updates as upserts. Da
Striim released the latest version 4.1.2The new version offers users tools to help with their streaming use cases, management tasks, and configurations, seeking to increase team collaboration with automation.Take a look at what is new in this version:New Features in Striim 4.1.2. New Features in Striim Cloud 4.1.2. Enjoy!
Real-time data analytics is proving to be the next big wave in Big data and Cloud computing and in a computing context, there’s an increasing demand to derive insights from data just milliseconds after it is available or captured from various sources.NoSQL databases have become widely adopted by companies over the years due to their versatility in handling vast amounts of structured and unstructured streaming data with the added benefit to scale quickly with large amounts of data at high user loads. Why MongoDB?MongoDB has become a prominent powerhouse among NoSQL databases and is widely embraced among many modern data architectures. With its ability to handle evolving data schemas and store data in JSON-like format which allows us to map to native objects supported by most modern programming languages. MongoDB has the ability to scale both vertically and horizontally which makes it a prime choice when it comes to integrating large amounts of data from diverse sources, delivering data
OverviewStriim is a next generation Cloud Data Integration product that offers change data capture (CDC) enabling continuous replication from popular databasessuch as Oracle, SQLServer, PostgreSQL and many others.In addition to CDC connectors, Striim has hundreds of automated adapters for file-based data (logs, xml, csv), IoT data (OPCUA, MQTT), and applications such as Salesforce and SAP. Our SQL-based stream processing engine makes it easy toenrich and normalize data before it’s written to targets like BigQuery and Snowflake.Traditionally Data warehouses that required data to be transferred use batch processing but with Striim’s streaming platform data can be replicated in real-time efficiently.Securing the in-flight data is very important in any real world application. A jump host creates an encrypted public connection into a secure environment.In this tutorial, we’ll walk you through how to create a secure SSH tunnel between Striim cloud and your on-premise/cloud databases with an
OverviewTransportation services like Uber and Lyft require streaming data with real-time processing for actionable insights on a minute-by-minute basis. While batch data can provide powerful insight on medium or long-term trends, in this age live data analytics is an essential component of enterprise decision making. It is said data loses its importance within 30 minutes of generation. To facilitate better performance for companies that hugely depend on live data, Striim offers continuous data ingestion from multiple data sources in real-time. With Striim’s powerful log-based Change Data Capture platform, database transactions can be captured and processed in real-time along with data migration to multiple clouds. This technology can be used by all e-commerce, food-delivery platforms, transportation services, and many others that harnesses real-time analytics to generate value. In this post, we have shown how real-time cab booking data can be streamed to Striim’s platform and processed
A discussion with Abhi Sivasailam – a Growth & Analytics leader (Flexport, Hustle, Keap, Honeybook) – on "The Modern Data Team" shortly before his talk at dbt Labs' Coalesce conference in New Orleans. Abhi dives into real world data leadership and engineering management topics such as applying Domain-driven design on data teams, producer-defined models (plus why he thinks they're better than consumer-led), and adhering to SLAs across the business. Abhi also provides a future-facing view of the data industry: eliminating arbitrary uniqueness in analytics engineering. Can we all align on common data models? Is this what 'modern data teams' will look like? Tune in to learn more!About Abhi Sivasailam:Abhi Sivasailam is a Growth & Analytics leader who previously led those functions at Flexport, Hustle, Keap, and Honeybook. He currently invests in and advises companies on their data strategies and coaches operators. Follow Abhi on Twitter for more insights.
Are there any examples of using Striim’s KafkaWriter to stream data to a topic in Confluent Cloud?
I have a JSON Field in my PostgreSQL database with this format I want to take all the fields in this flat JSON structure and turn each of them into fields in a typed stream. How would I go about doing that? I tried using the ‘makeJSON’ function on the JSON string column but I’m unable to run JSONNode function on it. Running the above query results in a ‘CRASH, cannot map’ exception.
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