A selection of the top articles and videos from the last week on SystemsDigest.com. Don't forget to check back regularly for daily updates from around the globe.
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In computer programming, a function is a set of instructions that can be invoked to perform a particular task. In object-oriented programming (OOP), a method is a function that is typically associated with an object and models its behavior . In Java, methods can also be static, in which case they are part of a class definition and do not require an object to be created before they are invoked.
Amazon S3 (Simple Storage Service) has been around since 2006. Most use this scalable, cloud-based service for archiving and backing up data. Within 10 years of its birth, S3 stored over 2 trillion objects, each up to 5 terabytes in size. Enterprises value their data as something worth preserving. But much of this data lies inert, in “cold” data lakes, unavailable for analysis. Also called “dark data”, it can hold key insights for enterprises. But the problem is, how do businesses access dark data for analysis in a scalable, efficient manner? That’s where Amazon Redshift Spectrum comes in.
As we head into the holidays, there’s no better time to talk about bringing people together. And there’s no better way to bring employees together within a company aspiring to be data-driven than with a data literacy program. What data analytics processes should your organization put into place to increase data literacy? It all starts with establishing a training program to empower your people to work with data, regardless of their level of expertise. Mastering this process, however, involves gaining executive buy-in, designing data literacy training for multiple levels, and effectively measuring and reporting on the initiative’s success.
As a new calendar year approaches, public sector CIOs and IT leaders are preparing for another year of change in their technology stack and its role in accomplishing their mission. The last two years have brought immense change and shifting imperatives to the public sector. Perhaps one of the most impactful is the drastic acceleration of digitization initiatives.
Express and Hapi are frameworks based on Node.js, an open-source server environment that runs on various platforms, using JavaScript as the language of choice. Since both frameworks are Nodejs-based and popularly used for web and mobile application development, let’s compare them and see which is more suitable for your needs as a developer.
For decades, the technologies and systems that deliver analytics have undergone massive change. What hasn’t changed, however, is the goal: using data-driven insights to drive actions. Insight to action has been a consistent vision for the industry. Everyone from data practitioners to technology developers have sought this elusive goal, but as Chief Data Strategy Officer Cindi Howson points out, it has remained unfulfilled — until now. The combination of the cloud ecosystem and open APIs have finally turned the tide, making it possible at long last to truly go from insight to action.
As an estimated 92% of enterprises have adopted hybrid and multicloud strategies, according to the 2021 State of the Cloud Report from Flexera, cloud operations (CloudOps) teams face increasing pressure to simultaneously manage costs while improving business outcomes. What levers can CloudOps teams pull to achieve operational objectives such as reducing hybrid and distributed cloud complexity, enhancing security, and automating processes? I put this question to cloud leaders from Hitachi Vantara and AWS in a virtual webinar last week. We convened during the week of AWS re:Invent to discuss the promises and pitfalls of CloudOps.
Enterprises using Microsoft Azure can now deliver secure, automated data pipelines while boosting compliance with privacy and industry mandates using Fivetran Business Critical. Join the thousands of companies using Fivetran to centralize and transform their data.
Organizations of all sizes and industries now have access to ever-increasing amounts of data, far too vast for any human to comprehend. All this information is practically useless without a way to efficiently process and analyze it, revealing the valuable data-driven insights hidden within the noise. The ETL (extract, transform, load) process is the most popular method of collecting data from multiple sources and loading it into a centralized data warehouse. During the ETL process, information is first extracted from a source such as a database, file, or spreadsheet, then transformed to comply with the data warehouse’s standards, and finally loaded into the data warehouse.
While Redshift is arguably the best data warehouse on the market, it can come with a hefty price tag. We’ve created this Redshift pricing guide to help you evaluate Redshift cheaply, create a budget for full implementation, and optimize your Redshift set up so that you’re getting the most bang for your data buck. Ready to get started? Think of this blog post as a “choose your own adventure” guide. Based on where you are in your journey, some parts may be more relevant to you than others, so please use the links above to jump to the sections that you’re most interested in. If you have any questions, leave a comment, and one of our support team members will get back to you.
When choosing any SaaS application, you must start with a clear understanding of your business requirements. Then ask yourself the following questions: Develop a framework for data processing requirements, and you'll find a data warehouse solution that provides the right amount of power, functionality, and high performance for data analytics. Keep the answers to these questions in mind when reading through this article.
The following is Part III of a three-part series. Welcome to the final installment of a three-part series discussing the areas to take seriously when you want to drive business with analytics. In Part I of this series, I discussed how to prioritize data accessibility and how to address the challenges that come with it. Those challenges include: Part II discussed where the disconnect is and addressed how organizations can bridge the gap. While old data governance programs may have felt like too much bureaucracy, more current options use artificial intelligence (AI) and machine learning (ML) to quickly sort and categorize data, to comply with security policies and data privacy standards.
Rails 7 is just around the corner. We don't have a confirmed release date, but it is expected to be available before Christmas, so not very long to go. The latest version as of this post's publication is 7.0.0.rc1, the first release candidate. Basecamp, HEY, Github, and Shopify have all been running the Rails 7 alpha in production, so we can expect even the release candidate to be pretty stable. In this post, we will look at some of the new features and changes that Rails 7 will bring.
Here at Appian, we have experienced tremendous growth in the past few years by helping our customers solve their most important business problems faster. We do this by providing a low-code platform that brings together humans, systems, and most recently, robots in support of any mission. As we’ve grown, we’ve added many productivity tools and endpoints to our global IT environment for employees to do their jobs, and AWS instances to the Appian Cloud for new customers to develop and run their applications on.
Node.js is a free, cross-platform JavaScript runtime environment, while single-threaded in nature, executes asynchronous code using several threads in the background. Because of its design, Node.js has received a lot of flak. It seems unusual that Node.js doesn't have direct access to threads when compared to programming languages like Java, C, or Python. The worker_thread module in Node.js 11 allows us to launch many threads on a single core. We could have used the --experimental-worker parameter to utilise this module in Node.js 10, but with Node.js 11, we can now skip it.
Ably is a platform for pub/sub messaging. Publishes are done on named channels, and clients subscribed to a given channel have all messages on that channel delivered to them. The Ably pub/sub backend is multi-region: we run the production cluster in 7 AWS regions, and channel pub/sub operates seamlessly between them. If you publish a message on a channel in one region, it is sent immediately to subscribers in the same region as you, and in parallel is broadcast to active instances of the same channel in other regions to reach subscribers in those regions.
Over the course of my next two blog posts, I would like to share my thoughts around a debate raging in data architecture circles. The bone of contention? That the 21st century needs a new data management paradigm for modern analytics. First up, I’ll frame the argument and explain the two prominent approaches of data hub and data fabric. Then, I’ll cover data mesh and compare all three architectures. As always, I’d love to get your input, feedback, queries and comments!
Business intelligence plays a critical role in making better decisions. By using reports and data visualizations to surface findings from a variety of data sources, BI tools can allow teams across an organization to access business data—without requiring technical expertise in SQL or coding.
Amazon Redshift makes it easier to uncover transformative insights from big data. Analytical queries that once took hours can now run in seconds. Redshift allows businesses to make data-driven decisions faster, which in turn unlocks greater growth and success. For a CTO, full-stack engineer, or systems architect, the question isn’t so much what is possible with Amazon Redshift, but how. How do you ensure optimal, consistent runtimes on analytical queries and reports? And how do you do that without taxing precious engineering time and resources?
One of the key cornerstones of the emerging field of ethical, explainable AI is recognizing and avoiding bias. As AI takes on a greater role in organizations with sometimes opaque calculations, there is an increased urgency in many businesses to get ahead of these challenges, and companies, such as IBM, Salesforce and Microsoft, have already added roles specifically with the aim of ensuring that ethics are a key consideration of AI.
Organizations that work in a highly regulated industry such as medical/health, pharmaceutical, security, automotive, aerospace, and defense, know how important it is to comply with standards and regulatory requirements. In order to meet regulations, manufacturers must maintain strict control over the development process, from testing to maintenance, and release. Xray test management app is a powerful test management app that can help you meet compliance and regulations in your industry. This article shows you how you can set up a process for compliance and risk management using Xray, so you can meet compliance, every step of the way.
Digital Transformation has ushered in a new era in business where every company is becoming a technology company (and with good reason). According to IDC, digitization has created a global market opportunity worth a staggering $20 trillion (over 20% of global GDP).
On December 3rd, the Elixir core team announced the latest Elixir release, 1.13. In this post, we'll explore some of the more exciting changes and new features, and discuss their impact on the Elixir community.
Here at Ably we consider software development to be in our DNA. We write and maintain a lot of code, much of it open-source, with full public visibility, and we host and manage it using the GitHub platform. The code is segmented into numerous discrete repositories, each with a specific scope and purpose. We've been using GitHub heavily since Ably was founded in 2016, so we have plenty of history, and we're committed to the platform. It has worked well for us: we love the GitHub ecosystem, and we've learned a lot about it as we've grown together.
One of the most challenging and complex elements of operating a financial services institution is compliance. Managing risk, security and privacy to earn customers’ trust has long been at the core of financial services, but this foundation has been shaken over recent years. A series of major incidences of fraud and compliance violations — such as the Nordic Laundromat scandal, which exposed several Nordic banks as part of billion-dollar Russian money laundering schemes — have eroded trust in the industry.
Adding traceability to design verification allows teams to definitively and easily document the exact state of the verification at any point in the design process. This provable documented status of verification is particularly important when trying to meet FuSa specifications like ISO 26262 and DO-254. In this blog, learn how the Methodics IPLM platform links requirements, design, and verification together to provide a fully traceable model between all three systems.
When a program encounters an exception during execution, it is terminated if the exception is not handled. By handling multiple exceptions, a program can respond to different exceptions without terminating it. In Python, try-except blocks can be used to catch and respond to one or multiple exceptions. In cases where a process raises more than one possible exception, they can all be handled using a single except clause. There are several approaches for handling multiple exceptions in Python, the most common of which are discussed below.
BigQuery is a powerful tool for data analytics and business intelligence, but it isn't very easy to get the most out of it without some help from ETL (extract, transform, load). In this blog post, we'll go over some of the best ETL solutions for BigQuery to help you make sense of your company's data.
The Python TypeError: unhashable type: 'list' usually means that a list is being used as a hash argument. This error occurs when trying to hash a list, which is an unhashable object. For example, using a list as a key in a Python dictionary will cause this error since dictionaries only accept hashable data types as a key. The standard way to solve this issue is to cast a list to a tuple, which is a hashable data type.
So, you’ve done your research on low-code platforms and learned about the speed, agility, and security they bring to application development. This sounds like a good direction for your organization, which means it’s time to learn more about the various low-code vendors and what they have to offer. But where to start? As with any growing market segment, you’ll find a rapidly expanding pool of low-code providers, many with similar products and services, and all with their own strengths and weaknesses. You’ll need a targeted approach, both to narrowing the vendor field, and to evaluating those vendors lucky enough to make your short list.
This blog was co-created by Ricardo Ferreira (Elastic) and Viktor Gamov (Kong). We love our microservices, but without a proper observability (O11y) strategy, they can quickly become cold, dark places cluttered with broken or unknown features. O11y is one of those technologies deemed created by causation: the only reason it exists is that other technologies pushed for it. There wouldn’t be need for O11y if, for example, our technologies haven’t gotten so complex across the years. Suppose your microservices have turned into this dark, scary place. In that case, finding the root cause of specific problems across several microservices might cause you to get as frustrated as the character Charlie Kelly from It’s Always Sunny in Philadelphia.
Yes, I’ve said it. The modern data stack is a pain to work with. But it wasn’t always like that. As companies realized they can leverage data to accelerate growth new data tools were invented. From NoSQL databases that specialize in processing specific data structures (graph anyone?) to the Python-Pandas-like Spark ecosystem that allows you to run queries on Big Data (capital B, mind you). But with every new tool added to the data stack, the complexity increased. We have so many data tools nowadays that the data technology stack is starting to look like the land of JavaScript libraries. Just. Too. Many.
As a company’s data assets grow, the need for cloud computing increases in tandem. For keeping pace with this growth, Snowflake stands above the rest. What makes Snowflake so special? This cloud-agnostic platform takes the best of traditional database technology and combines it with modern cloud computing to drive the agility and innovation companies need to remain competitive. It features on-the-fly scaling, flexible clustering options, and the capability to hold several petabytes of information. Not only that, its unique architecture offers more cost-efficiency than traditional data warehouses. This overview discusses what is Snowflake, its benefits, and how it fits into your ecosystem.
