Eigentlich endet Julian Assanges Freiheitsstrafe am 22. September. Eine Richterin entschied jedoch, dass er auch nach dem Verbüßen seiner Haftstrafe im Gefängnis bleiben muss. (Julian Assange, Wikileaks)
Quelle: Golem
Nach acht Jahren ist Disney-Chef Bob Iger aus dem Verwaltungsrat von Apple zurückgetreten – pünktlich zur Ankündigung von Apple TV+. Die Konkurrenz zum eigenen Disney Plus dürfte den Manager zu der Entscheidung bewegt haben. (Apple TV+, Apple)
Quelle: Golem
Der Sperrbildschirm in iOS 13 kann mit einem einfachen Trick umgangen werden. So kann auf das Adressbuch des Besitzers zugegriffen werden. iOS 13 soll am 19. September veröffentlicht werden – die Lücke möchte Apple bis dahin nicht schließen. (Apple, Instant Messenger)
Quelle: Golem
Das neue, günstige iPad hat eine Display-Diagonale von 10,2 Zoll – und ist damit zu groß, um Microsofts Office-Apps kostenlos verwenden zu können. Die Grenze liegt bei 10,1 Zoll, die 0,1 Zoll zu viel werden für die Nutzer nicht ganz billig. (iPad, Apple)
Quelle: Golem
Bislang sind die Sojus-Raumschiffe Europas einziger eigener Zugang zum All. Seit einigen Jahren arbeitet die Esa aber an einem eigenen Raumgleiter, dem Space Rider. Anfang des Sommers passierte das Projekt einen wichtigen Meilenstein. Das lässt hoffen, dass es nicht wie seine Vorgänger scheitert. Von Friedrich List (ESA, Raumfahrt)
Quelle: Golem
An sofort können Sie mit AWS Elemental MediaTailor Live-HLS Video Streams ohne SCTE-Infrastruktur monetarisieren. Mithilfe von MediaTailor können Content-Besitzer Pre-Roll-Werbeanziegen vor dem Beginn des Streams einschalten, wobei der Beginn des Live-Streams mit personalisierten Werbeanzeigen überlagert wird, um jeden Stream-Start zu monetarisieren. Dies fördert eine hochrentable Monetarisierungsstrategie mit geringer Investition.
Quelle: aws.amazon.com
Amazon SageMaker unterstützt jetzt eine bessere Steuerung und einen besseren Zugriff mit Amazon SageMaker-spezifischen Bedingungsschlüsseln Sie können diese neuen Schlüssel im Bedingungselement einer IAM-Richtlinie (Identity and Access Management) verwenden, um die Bedingungen, unter denen die Richtlinienanweisung gilt, weiter zu verfeinern.
Quelle: aws.amazon.com
AWS GuardDuty ist jetzt in der AWS-Region Naher Osten (Bahrain) verfügbar Sie können jetzt kontinuierlich Sicherheitsbedrohungen in der Region AWS Naher Osten (Bahrain) überwachen und erkennen, um Ihre AWS-Konten und -Workloads zu schützen.
Quelle: aws.amazon.com
Editor’s note: Today we hear from Zohaib Ali Khan, head of mobile financial services, and Nadir Ikram, technical lead at the National Bank of Pakistan (NBP), the country’s largest government-owned bank. Read on to learn more about how NBP uses APIs to help implement digital banking and reduce the burden of legacy manual processes.NBP, Pakistan’s largest government-owned bank, serves private and commercial customers and also acts as the government treasury bank. This means that it handles all government transactions—including disbursements and cash collection. In the past, every government transaction had to be handled physically through the NBP branch network. But in a populous country like Pakistan, managing the huge volume of financial transactions is a big task, especially if a single bank is the only conduit. While we’re still in the early stages, here are a few ways that we’re actively working to find solutions that overcome these challenges. Using APIs to increase accessOur digital banking implementation team, which includes product developers and a small in-house think tank, is responsible for developing new technology and out-of-the-box solutions tailored to the requirements of different areas of the bank. Recently, we developed a plan to open up the NBP government mandate to other banks and third-party fintech partners. Under this new model, instead of relying solely on our own channels, customers can now transact through fintech apps and other approved Pakistani banks. To be able to roll out a solution that would be reliable, scalable, and secure enough to meet our needs, we adopted Google Cloud’s Apigee API management platform. This platform allows us to accelerate our product development, so that we can compete in the fintech space. It also gives our customers access to a wide range of banking services through our own and partner channels.As a government bank, NBP has to deal with a lot of procedural hurdles, which have slowed down our entry into the fintech market. Additionally, legacy systems required us to develop solutions for each particular channel and use case. APIs and API management increase the reusability of our services, while also bringing ease and speed of getting our services to market. In fact, we’ve seen the time it takes to offer a new solution reduced by 20%. Apigee not only helps us to achieve our go-to-market goals—it enhances our capacity to capture new and unique use cases across multiple channels. We appreciate the speed, agility, and security that Apigee brings us, along with its many out-of-the-box features. Reducing barriers to consumer servicesOne example of how we’re using Apigee is our passport collection use case. In Pakistan, to obtain a passport, citizens have to visit an NBP branch. This can involve waiting in long lines to deposit the government-required passport insurance fee. The sheer volume of these transactions was overwhelming our local branch teams, and costs for this type of manual transaction were high. Furthermore, the government had problems reconciling the fee collection with the passport transactions. To address these concerns, we developed an API that allows not only NBP branches, but also third-party banks and fintechs, to accept passport issuance fees.Customers can now visit the bank or fintech provider of their choice, reducing the load on NBP, while the government passport department inspectors can now easily reconcile these transactions. We also developed a bill payment solution with Apigee that gives customers the possibility of paying utility bills online. Previously, the NBP process was inefficient. We had an in-person bill payment mechanism operating in our 1,500+ bank branches. Now, we’ve integrated the payment API with the branch channel so that bill payment is automated, whether it takes place in a branch or online. Increasing the API footprint inside and outside the bankNow that we’ve implemented the Apigee platform, our partner ecosystem is benefiting from it and growing, too. We’ve enrolled a wide range of fintechs that are developing products in the corporate payment space. We’re also planning to partner with several incubation centers to provide our APIs via sandbox environments, once our Apigee developer portal goes live. Other fintechs will be enrolled through the incubation centers as well. Finally, alternative third-party partners, such as fintechs and banks, will consume our APIs and/or partner with us for product development.We’ve currently published 10 APIs and by the end of this year, we expect to have 25 to 30 live. We’re looking forward to implementing monetization, but not necessarily with revenue as the primary focus. As NBP is a government-owned bank, we have a responsibility to act as a catalyst for smaller players, and we see monetization expertise as an opportunity for fulfilling our mandate to help fintech startups grow and mature. In the long run, API revenues will certainly become more important, but the short-term goal is seeding innovation in the marketplace and providing the best possible retail banking experience for our customers, nationwide.To learn more about API management on Google Cloud, visit the Apigee page.
Quelle: Google Cloud Platform
Editor’s note: Today we’re hearing from the founder of Quantum Metric, a digital intelligence platform that analyzes huge amounts of digital customer data to improve the customer experience, enhance sales, and increase loyalty. The company credits a huge leap in innovation—along with a 10-fold increase in business—to their decision to adopt Google Cloud. Here’s more detail on how Quantum Metric uses Google Cloud’s BigQuery.At Quantum Metric, we’re in the business of bringing our customers business insights that are based on customer experience data and analytics for mid-market and Fortune 500 companies. Our software, powered by big data, machine intelligence, and Google Cloud, helps our customers identify, quantify, prioritize and measure opportunities to improve digital experiences. As companies move to a more agile product lifecycle, including continuous deployment and continuous integration, they’re finding that it’s critical to receive perpetual quantified feedback and insights from their data in real time to understand where the largest opportunities exist. Each year, billions of customer interactions are captured through browsers or mobile apps on PCs, tablets, and mobile devices. This data, fed into the Quantum Metric platform, can show if a customer had a password problem they couldn’t solve or struggled when trying to purchase something and abandoned their cart. It also can show if the customer tried in vain to complete an online change to their service provider’s subscription, to reach tech support, or couldn’t find the size or color they were looking for while shopping online. Most importantly, the Quantum Metric platform quantifies the business value of the issue, helping organizations prioritize where they can make the largest impact to their business.Success overwhelms our initial architectureInitially, the Quantum Metric experience analytics software ran on a MySQL open source relational database management system (RDBMS). The MySQL RDBMS worked great for simple queries, when there was a specific question to ask of the data. Soon, though, we knew we needed to offer more advanced data science capabilities. Our bigger customers wanted to ask questions across very large data sets—days, weeks, months, and years worth of data. They wanted to pose iterative questions using complex filters to answer their most challenging business questions. With more complex queries across more data, response times from our RDBMS went from 100 to 500 milliseconds to as long as 20 minutes. That delay was slowing down our ability and time to insights, which also reduced the value we could provide to our customers, since iterative exploration and analysis requires real-time query responses. Because of the need for real-time responses, there were certain questions that we just weren’t able to ask of the data. It became clear that we needed a much more robust data warehouse solution. There were also operational challenges with MySQL and massive-scale data ingestion. We spent a lot of time into the wee hours of the night and morning handling errors and recovering databases. We tried to address these challenges by sharding, partitioning, and indexing the data to optimize for the types of questions customers were asking. But the problems were escalating and happening more often, from once a month across the customer base to monthly for at least 20 different customers. We could tune the platform for today and tomorrow’s workload, with good guesses at where indexes could be used, but we simply couldn’t continue to horizontally scale MySQL in a cost-efficient and operationally efficient manner. Speed breeds innovationOnce we started exploring options that could better scale with our business, we looked at NoSQL technologies like Cassandra (a partitioned row-store database), MySQL’s Column Store (a columnar store database), and Vertica (a columnar store database)—each with unique ways of handling data storage and accessibility. But with high volumes of complex queries across large data stores, all of these solutions began to fail, bogged down with multiple, simultaneous users. We could have solved the problems with more raw compute and storage, but it would have been prohibitively expensive to run and require a large team to operate. We then decided to try BigQuery, and it was transformative. We connected our front end to BigQuery via APIs. Once data is 15 minutes old, it is automatically extracted, loaded, and transformed (ETL) to BigQuery. We continuously update the legacy MySQL RDBMS so its data is integrated with BigQuery data when queries require real-time data. Most query response times are within 100-200 milliseconds, matching what we initially experienced with MySQL. When traffic from our customers scales up, we can now scale on-demand to accommodate it, thanks to BigQuery’s hundreds of thousands of CPUs. Our customers no longer run into slow response times, and we’ve gained confidence that we can offer them—and their users—advanced insights and better experiences without delay. More importantly, with this scale of query power, we were able to build data science algorithms into the platform, which iteratively query BigQuery based on the results, and help quantify the impact of a specific issue to a specific segment of users. Adding these capabilities was possible because of the massive scale of BigQuery. In addition to new insights and fast response times, we wanted our customers to be able to ask complex questions using very simple language. For example: “Show me high-loyalty customers, located in specific geographic areas, who visited the web site at least five times, based on specific campaigns, and never booked a seat on a flight.” This was exactly the kind of query that was used by a major U.S. airline to understand the multi-million dollar impact of a failure affecting their most valuable customers: their high-loyalty members. And this was all done while maintaining the highest standard of care of customer data and privacy by default, using multiple layers of encryption of data in transit, at rest, and a unique military-grade encryption approach. This approach encrypts PII, including even session cookies, with a RSA-2048 key available only to a select few and used for use cases such as fraud analysis. It’s no exaggeration to say that BigQuery has totally transformed our business. It provides the petabyte scale and speed we were missing, in addition to taking care of operational maintenance, a task that was burying our team with MySQL. We’re now able to support some of the largest companies in the world that require real-time, petabyte-scale analytics. That lets them serve more customers faster with higher quality, and take advantage of BigQuery’s power and scale to innovate. There are other cloud solutions that can address petabyte analytics, but the most unique value proposition of BigQuery was its on-demand scaling and operational management, with extremely cost-effective pay-as-you-go billing. While today we are at a scale where we have round-the-clock querying needs, our early days had very sporadic query loads where we needed instant scale, then a long lull of nothing. The unique business model of BigQuery’s pay-by-bytes-scanned allowed us to have access to a massive-scale querying platform without breaking the bank. Using BigQuery powers better customer experience and reduces purchasing frictionAmong the many features of Quantum Metric is the ability to replay online customer sessions. In the example below from a mobile e-commerce site, each action is displayed chronologically. Why did this customer’s transaction fail? Diving deeper, the session replay shows that the user tried to change the item quantity in the checkout cart, which resulted in a failed API call. Powered by BigQuery, Quantum Metric can then show how many other end users had this issue, with a simple click of “Show More Errors Like This.” With BigQuery’s massive scale, Quantum Metric will then quantify the impact of that issue, so companies can prioritize which issues need attention immediately. If this is the issue that’s impacting the business the most, our customer can use a single click to open a Jira ticket, forwarding the discovery to their product and engineering teams. Those teams can then re-engineer the experience in near-real time, addressing the failed API call and cutting out the frustrating time it takes for engineers to reproduce the issue.Once we had a powerful back end in BigQuery, we realized that Quantum Metric’s platform could be used to ask complex questions from vast datasets. We built some of the processes that data scientists use to formulate those queries right into our product. For example, we added one series of processes to our platform to help customers understand whether a suspect issue is really impacting end-user experience. Is it something that should be prioritized and fixed? Does this really affect the user experience? Does it have financial impact? These and other questions can be pre-defined as a complex query in Quantum Metric to let our customer quickly gain insight on how an issue is impacting the business. Customers were blown away when they heard this was possible. It was the holy grail for what they were looking for in data science. It really sets us apart from our competition.Today, with every company heavily dependent on data, those companies that can uncover and act on insights fastest are the ones that will succeed. BigQuery gives us the data warehouse platform we need to provide our customers with fast, reliable technology tools. It frees us from having to deal with the minutiae of technology infrastructure operations, so we can focus on finding and extracting the magic in customer data. With the power and scale of BigQuery, combined with the real-time capture of every user experience with 100% fidelity, we’re able to offer a self-service analytics platform that provides insights into digital journey friction points and acts as the indisputable arbitrator of truth. Learn more about Google Cloud, and learn more about Quantum Metric.
Quelle: Google Cloud Platform
