First, custom contexts are now generally available for all LaunchDarkly customers. With this, organizations are enabled to create several target objects which can map to a business’s use case.

This provides users with the ability to deliver targeting how they want as well as offers improved control and business alignment for how features are delivered. 

The release of custom contexts also allows for multiple new use cases for LaunchDarkly Experimentation customers, such as the ability to create rules and build experimentation audiences based on different context types. 

In addition, the possibility to randomize experiments on known variables such as device type, browser type, and more, has been expanded. According to LaunchDarkly, this keeps the experiment stable as well as eliminates randomization discrepancies that result from traditional user targeting.

Next, an approvals dashboard has been released in order to assist users in tracking and managing approvals so that they can visualize all approvals in one single dashboard with options to sort and filter by. These options include necessary approver, approval requester, project status, and approval status.

The company has also updated its notification settings, allowing users to control what notifications they would like to receive, such as flag updates or approvals, and how they would like to receive them. 

Furthermore, LaunchDarkly announced a native integration with Microsoft Teams. This enables users to receive personal flag update notifications in real-time, associate chat messages with a flag via flag links, create and subscribe to channels that push flag update notifications, and get notified of approval requests right from within Microsoft Teams.

New RUM integrations for Datadog and AWS users are also available so that Datadog customers can now improve their RUM data with feature flag data from LaunchDarkly.

The team stated that this integration offers users better visibility into which flag variations end-users are experiencing and the impact that those variations have on overall engagement and application performance.

LaunchDarkly also added support for SCIM user and role provisioning in order to enhance the LaunchDarkly and Azure AD SSO integration. With this, users gain more control over provisioning with the real-time updates that SCIM offers, such as automatic de-provisioning.

Identifying flag owners has also been simplified with the addition of the ability to assign a flag maintainer. This allows for the filtering of the flag dashboard to flags maintained by teams, and further promotes flag accountability.

Dark theme is also now generally available in LaunchDarkly as an additional customization option. 

Lastly, the company has reached fedRAMP authorization. With this, government agencies and their commercial partners can utilize the Federal instance of LaunchDarkly’s feature management platform to deliver software and modernize applications with improved control.

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The goal of this release is to minimize the risk that SaaS-to-SaaS presents by allowing teams to manage sanctioned apps and discover apps that have access to the company’s data.

“As SaaS app dependency grows, so too does our comfort level in using these apps — this is why many grant access without considering the possible consequences. As a result, third-party app access has become the new executable file,” said Maor Bin, co-founder and CEO of Adaptive Shield. “Now, with these new capabilities, whether employees have connected 50 to 5000 apps, Adaptive Shield equips security professionals with the solution to regain control over their SaaS Security.”

According to the 2022 SaaS Security Survey Report from Adaptive Shield and the Cloud Security Alliance, 56% of organizations adopting SaaS applications said that their number one concern is the lack of visibility into connected apps. 

These new capabilities are intended to address this challenge that becomes exacerbated when employees connect several SaaS applications to the core stack without understanding the security risks or checking with the security team.

For more information, visit the website. 

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On top of this, the company has released a new study that demonstrates the need for a single, enterprise-wide SaaS DevOps platform with annual SaaS spending reported as $125 billion in 2021. 

According to the company, this platform brings disparate and siloed data together under a singular focus and alleviates the need to build separate development teams, metadata, configurations, profiles, permissions, and packages and release management tactics for end-to-end visibility spanning the SaaS DevOps ecosystem.

Additionally, Opsera’s multi-SaaS and multi-cloud architecture works to allow enterprises to leverage the platform in order to accelerate SaaS DevOps maturity in several SaaS applications. 

Enterprises are also enabled to leverage Opsera’s no-code platform and reusable microservices to improve the velocity of releases. 

“The increased pace of SaaS and the public cloud replacing on-premises solutions will have a global impact on IT operations,” said Vernon Keenan, senior industry analyst at SalesforceDevops.net and author of the study. “As organizations add more critical SaaS applications to their inventories, the need for a cohesive SaaS management strategy, or SaaS DevOps, will grow dramatically in the next five years. With today’s announcement, Opsera is well positioned to help enterprises to streamline their SaaS DevOps and help improve their agility, velocity, security posture and visibility.”

Based on Keenan’s report, SaaS revenue will continue to grow at a 25% rate annually and will reach $279 billion by 2024. The study also showed that 35 new SaaS companies have entered the market since 2018, demonstrating the part that SaaS application expansion has in this growth. 

To learn more and read the full report, see here.

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According to the company, the solution helps by saving developers time and providing them the foundational capabilities to onboard users including provisioning infrastructure for tenants,  monitoring consumption trends, configuring tenant profiles, integrating with a billing system and surfacing key metrics. 

RELATED CONTENT: SaaS backup: A more scalable way to ingest cloud app data

“Think of AWS SaaS Boost like a space launch system for your applications, with all the ground operation and rockets to help you propel and manage your software as a service in the AWS cloud. SaaS Boost significantly offloads development effort by accelerating application transformation to SaaS, freeing up software developers to focus on features that differentiate their product,” Adrian De Luca, the head of partner solution architecture of global ISV build programs at AWS, wrote in a blog post that contains additional details. 

It also includes integrations with other AWS services such as AWS CloudFormation, AWS Identity and Access Management (IAM), Amazon Route 53, Elastic Load Balancing, AWS Lambda and Amazon Elastic Container Service (Amazon ECS).

Since the project was released under the Apache 2.0 license, the code can be customized to meet business requirements. Developers can build connectors to technologies such as OAuth for authorization, Open Policy Agent for control, and OpenTelemetry for observability and more, De Luca explained. 

AWS is also working on building a charter and a set of guiding principles so that users can get the most out of SaaS Boost. 

“Our objective with AWS SaaS Boost is to get great quality software based on years of experience in the hands of as many developers and companies as possible,” De Luca wrote.  “Through a community of builders, our hope is to develop features faster, integrate with a wide range of SaaS software, and to provide a high quality solution for our customers regardless of company size or location.”

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Cleanshelf’s software provides an automated view of all SaaS applications in an enterprise, simplifies management and enables resource optimization, according to the company. 

LeanIX went on to explain that while SaaS adoption is rapidly increasing, it’s happening in a decentralized way, making it difficult to manage costs, security risk and data privacy.

RELATED CONTENT: The resurgence of enterprise architecture

Cleanshelf believes by automatically identifying SaaS applications throughout the enterprise and detecting unused licenses, it can minimize costs by about 15% in the first year. It’s tool is based on integrations with Enterprise Resource Planning (ERP) tools, spend and contract management solutions as well as identity and single-sign-on (SSO) providers.

“This technology is a perfect match for LeanIX, and as of today, we are adding Cleanshelf’s 40 team members spread across San Francisco, Denver, and Ljubljana to our team. Together we will grow as one to deliver a single outstanding product experience to customers and partners,” André Christ, co-founder and CEO of LeanIX, wrote in a post.

LeanIX is also helping DevOps and engineering teams manage the growing complexity of microservices with the newly announced Microservice Intelligence. 

“Increasing the number of microservices in any organization calls for DevOps teams to manage them across the landscape to achieve development efficiency. It also means companies need to invest in new tools to manage their microservice landscape. At a minimum, these tools must provide visibility across the microservice landscape with automatic discovery and cataloging capabilities, as well as provide insights to measure development KPIs such as deployment frequency,” Marion Richter, directly or product marketing at LeanIX, wrote in a post. 

Microservice Intelligence automatically creates a microservice catalog to provide more transparency and visibility into cloud-native apps, ownership and dependencies. 

In addition, it monitors development frequency, MTTR and failure rates of software services; and automatically maps libraries and versions to microservices to identify and prioritize patches and reduce open-source vulnerabilities. 

“Continuous transformation does not stop by migrating to the cloud,” said Christ. “Virtually every company is now a tech company and increasingly is delivering in-house software. Microservice Intelligence helps customers navigate the microservice landscape and reduce the complexity that occurs when decomposing the monolith into more loosely coupled services.”

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The new offering enables teams to execute codeless test scripts on real devices for native Android and iOS mobile apps, with web support coming soon. Later this year, a test case management product will also launch, according to the company. 

“Customers today are won and lost through digital experiences. That is why the quality of digital assets – mobile apps, websites, voice-driven experiences and more – is critical to get right,” said Doron Reuveni, the CEO of Applause, founder and chairman of the board. “To meet these needs, Applause continues to disrupt the testing market, having evolved from a services and solutions company to one that delivers a complete platform for driving product excellence.”

Hasura 2.0 released
Haura release version 2.0 of its open-source GraphQL Engine and the release enables organizations to deploy REST and GraphQL APIs from one configuration.

This release includes a GraphQL API gateway which provides granular authorization to any GraphQL API and it also supports Google’s BigQuery database in addition to PostgreSQL, SQL Server and MySQL.

Hasura also announced the managed service offering of GraphQL Engine, Hasura Cloud, now has AWS VPC peering capability to securely connect their data and infrastructure to Hasura Cloud in a secure private network.

Planview acquires PPM providers Clarizen and Changepoint
With the acquisition customers of all three platforms will benefit from a premier community of project portfolio management and professional service automation practitioners. 

“The nature of work has changed significantly in recent years, causing leaders across industries to rethink how to best strategically plan, execute, and empower teams in today’s all-digital world. This shift has placed a spotlight on the growing importance and strategic value of Portfolio Management, Work Management and Enterprise Agile Planning capabilities, as evidenced by the recent wave of IPOs, consolidation, and acquisitions of several key players in this category,” Planview wrote in a post.

The acquisition closely follows Planview’s acquisition by TPG Capital and TA Associates in December 2020. 

Pega’s enhancements for low-code mobile app development
The newest features of Pega Mobile provide UX and app authoring enhancements, customizable branding options, and expanded offline capabilities in which Pega mobile apps are designed to be used offline and synced later when an Internet connection is restored.

“Pega Mobile makes it fast and easy for the user to create and manage as many mobile apps as the business needs. Instead of deprioritizing mobile, organizations can adopt a true mobile-first approach with powerful apps that help make them more productive,” said Eric Musser, the general manager of intelligent automation at Pegasystems. 

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 It’s no wonder cloud applications like CRM, Support, ERP or e-commerce tools, have become prime targets for DataOps teams looking for answers about what and why certain things are happening. After all, think about how much business data converges in a CRM system – particularly when it’s integrated with other business systems. It’s a mastered data goldmine!

DataOps teams often identify a high-value target application, like a CRM system, and then explore ways to capture and ingest data from the application via the system’s APIs. In the case of, say, Salesforce, they might explore the Change Data Capture and Bulk APIs. Various teams with different data consumption needs might then use these APIs to capture data for their particular use case, inevitably leading to exponential growth in data copies and compliance exposure. (After all, how do you enforce GDPR or WORM compliance for data replicas tucked away God knows where?!). 

When they encounter API limitations or even application performance issues, DataOps teams then start to replicate data into nearby data lakes. This enables them to create centralized consumption points for the SaaS data outside of the application. Here, storage costs are more favorable and access is ubiquitous. Here, teams typically take a deep breath and start a more organized process for requirements gathering, beginning with the question of “who needs what data and why?”

Meanwhile in a parallel world, IT teams implement data backup strategies for those same cloud applications. If something bad happens (say, data corruption), these critical business systems need to be rapidly recovered and brought back online to keep the business going. Here, standard practice is to take snapshots of data at regular increments either through DIY scripts or with SaaS backup tools. In most scenarios, the backup data is put in cold storage because… well, that’s what you do with data replicas whose sole purpose is to act as an ‘insurance policy’ in case something goes wrong.

 With all of these teams trying to consume the same data in the same organization, it makes sense that costs and maintenance cycles quickly spiral out of control. For every TB of production data, ESG identified that another 9 TB of secondary data is typically generated – rapidly offsetting any cost savings due to ever-decreasing storage costs on public clouds.  

So why are we inflicting this 9X+ data multiplier on ourselves?

One reason is convenience. It’s just easier to walk up, grab what we need and walk away. But convenience can often come at the cost of quality, security and risk: how you do you the data you are grabbing is the best possible dataset the organization has on a particularly entity? This question is particularly important in organizations that have strong data mastering initiatives. If your replicas contain sensitive data that you are tucking away in some generally unknown place, are you expanding the attack surface area for the organization? Are there governance or compliance regulations that your data may fall under?

Another reason is because “we’ve always done it this way.” The status quo of thinking about backup data as an insurance policy that is separate and unrelated to SaaS data ingestion for other scenarios, reaches back before the days of SaaS applications themselves – when data backup and ingestion were two separate motions done on the database level.

How we do things is just as important as doing them in the first place. And changing HOW we do things is hard. It starts with the realization that the status quo no longer applies. In this case, the realization that cloud applications allow for fundamentally different data consumption patterns – and that backup tools can be the perfect hat trick to take back ownership and control of your cloud application data, and to re-use backed up data for all other data consumption needs across our organizations.  

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KronoGraph is a developer toolkit for JavaScript and React developers that enables users to build browser-based, interactive, investigative timeline visualizations that reveal how events unfold.

It is entirely customizable and works with any source of time-based data in datasets. While KronoGraph is a standalone tool, it integrates with Cambridge Intelligence’s graph visualization toolkits to give users two views of the same data.

“KronoGraph gives organizations a brand new way of understanding how events unfold over time. It’s been clear from the very first prototype that a timeline visualization tool was something analysts and investigators need and have been waiting for,” said Joe Parry, the founder and CEO of Cambridge Intelligence.

Diffblue launches automated Java unit testing solution
Diffblue announced the general availability of Diffblue Cover and Diffblue Cover: Community Edition, a free version created for developers using IntelliJ.

Diffblue Cover automates the burdensome task of writing Java unit tests, a task that takes up as much as 20 percent of Java developers’ time, at speeds 10 to 100 times faster than humans, according to the company.

“We help organizations reduce time to ship, ship more often and ship code with fewer defects while freeing up developers to focus more time on the most engaging part of their jobs,” said Mathew Lodge, the CEO of Diffblue. “Companies on the journey to digital transformation, especially during this COVID pandemic, can’t keep up with the rapid software changes required to reorient their businesses. Automating unit testing on new and legacy code can accelerate their journey to success.”

SaaS Ventures announces second $20 million fund
Launch of Fund II is driven by increasing demand for investment in enterprise tech startups that are creating solutions for the new economy.

“We are incredibly excited to announce the launch of our second fund and continue our mission of supporting talented founders who are innovating the way products are created, moved, secured, and sold,” said Brian Gaister, co-founder of SaaS Ventures. “This is a big moment of validation for our model, and we look forward to expanding the work that we are accomplishing alongside our partner funds.”

Additional details are available here.

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The solution creates “real-time digital twins” that simultaneously analyzes telemetry from thousands of streaming data sources to provide customers deeper introspection without waiting to query data at rest in data lakes. It also offers connections to streaming cloud data sources, including Microsoft Azure IoT Hub, Amazon Web Services (AWS) IoT Core, Apache Kafka and REST.

“We built the ScaleOut Digital Twin Streaming Service to help our customers dramatically improve situational awareness in their live systems, spanning thousands or even millions of data sources, said William Bain, founder and CEO of ScaleOut Software. “Whether tracking a fleet of rental cars or a population of smartwatch users, real-time digital twins are a game changer for streaming analytics.”

Real-time digital twins track the dynamic state of individual data sources, using this information to enhance the immediate analysis of incoming telemetry.  

Real-time digital twin analytics can offer benefits for a variety of industries. It can track thousands of intrusion points, meters and detectors to filter telemetry and quickly identify issues or threats as they evolve, interpret telemetry from a population of smart devices, such as blood-pressure and heart-rate readings, enhance tracking of large vehicle fleets, store or restaurant chains, or thousands of assets.

For ecommerce, the solution can analyze website click-streams for ecommerce shoppers to improve product recommendations based on each shopper’s dynamic selections, demographics, brand preferences and recent purchases.

Other features include in-memory computing power, ability to analyze and response to incoming event messages, a web-based UI and a toolkit for constructing and deploying real-time digital twin models with minimum app code.

“With its ability to immediately analyze data in motion from individual data sources in milliseconds and make immediate use of dynamic context for each data source, the ScaleOut Digital Twin Streaming Service can fundamentally change the way industries like real-time monitoring, healthcare, logistics, retail and financial services process live data streams to make critical decisions in the moment,” ScaleOut wrote in a post.

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“Developers spend huge amounts of time writing custom integrations so they can pass data between SaaS applications and AWS services so that it can be analysed; these can be expensive and can often take months to complete,” Martin Beeby, principal advocate for AWS, stated in a blog post. “If data requirements change, then costly and complicated modifications have to be made to the integrations.” 

To solve these issues, Amazon AppFlow was designed to automate the data flows between AWS services and SaaS applications such as Salesforce, Zendesk, and ServiceNow.

RELATED CONTENT: Modern Analytics on AWS

Users can transform and process the data by combining fields to calculate new values, filter records to reduce noise, mask sensitive data to ensure privacy, and validate field values to cleanse the data, according to Beeby. They can also configure multiple types of triggers for their data flows, including one-time on-demand transfers, routine data syncs scheduled at pre-determined times, or event-driven transfers when launching a campaign within just a few clicks on the console. 

This allows application admins, business analysts, and BI specialists to implement most of the integrations they need without waiting for IT to finish integration projects. It also allows data to flow from SaaS applications to AWS service with data encryption while in motion.

Amazon AppFlow also works with AWS PrivateLink to route data flows through the AWS network instead of over the public Internet to provide stronger data privacy and security.  

“The service automatically scales up or down to meet the demands you place on it, it also allows you to transfer 100GB in a single flow which means you don’t need to break data down into batches,” Beeby wrote.

Amazon AppFlow now has support for S3 and 13 SaaS applications as sources of data, and S3, Amazon Redshift, Salesforce, and Snowflake as destinations, and Amazon plans on adding hundreds more. 

“Our customers tell us that they love having the ability to store, process, and analyze their data in AWS. They also use a variety of third party SaaS applications, and they tell us that it can be difficult to manage the flow of data between AWS and these applications,” said Kurt Kufeld, vice president for AWS. “Amazon AppFlow provides an intuitive and easy way for customers to combine data from AWS and SaaS applications without moving it across the public Internet. With Amazon AppFlow, our customers bring together and manage petabytes, even exabytes, of data spread across all of their applications – all without having to develop custom connectors or manage underlying API and network connectivity.”

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