Notes about Cutting-Edge Technologies and Everything

From the first glance, you can see that there are seven key Subject Areas where the Subject domains are located. These are: Data Data and System Design Technology, Governance Data Quality Security Related Capabilities. You can see that the difference in approaches to define the key Domains are rather big. It is not the purpose of this article to deliver a detailed analysis, but there is one striking observation I would like to share: the Subject domains and deliverables of these domains are being mixed with one another.  For example, let us have a look at Data governance. The domain ‘Data governance’ exists in four different models. Some other domains like ‘Data management strategy’, that appears in three models, is considered as a deliverable of Data Governance domain in other models, for example in DAMA model. Such a big difference of opinions on key Subject domains is rather confusing. Subject domain dimensions Subject domain dimensions are characteristics of (sub-) domains. It ...

Our aspiration to augment and improve every aspect of business and life with data, demands a paradigm shift in how we manage data at scale. While the technology advances of the past decade have addressed the scale of volume of data and data processing compute, they have failed to address scale in other dimensions: changes in the data landscape, proliferation of sources of data, diversity of data use cases and users, and speed of response to change. Data mesh addresses these dimensions, founded in four principles: domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance. Each principle drives a new logical view of the technical architecture and organizational structure. The original writeup, How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh - which I encourage you to read before joining me back here - empathized with today’s pain points of architectural and or...

Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture without any design tools. You can also describe or visualize the existing system architecture as well. Diagram as Code allows you to track the architecture diagram changes in any version control system. Diagrams currently supports six major providers: AWS, Azure, GCP, Kubernetes, Alibaba Cloud and Oracle Cloud. It now also supports On-Premise nodes. diagrams.mingrammer.com >>>

Modern world data and its usage has drastically changed when compared to a decade ago. There is a gap caused by the traditional ETL processes when processing modern data. The following are some of the main reasons for this:  Modern data processes often include real-time streaming data, and organizations need real-time insights into processes.  The systems need to perform ETL on data streams without using batch processing, and they should handle high data rates by scaling the system. Some single-server databases are now replaced by distributed data platforms ( e.g., Cassandra, MongoDB, Elasticsearch, SAAS apps ), message brokers( e.g., Kafka, ActiveMQ, etc. ) and several other types of endpoints. The system should have the capability to plugin additional sources or sinks to connect on the go in a manageable way. Repeated data processing due to ad hoc architecture has to be eliminated. Change data capture technologies used with traditional ETL has to be integ...

The CCM, the only meta-framework of cloud-specific security controls, mapped to leading standards, best practices and regulations. CCM provides organizations with the needed structure, detail and clarity relating to information security tailored to cloud computing. CCM is currently considered a de-facto standard for cloud security assurance and compliance. Download >>>

Data produced by applications, devices, or humans must be processed before it is consumed. By definition, a data pipeline represents the flow of data between two or more systems. It is a set of instructions that determine how and when to move data between these systems. My last blog conveyed how connectivity is foundational to a data platform. In this blog, I will describe the different data processing pipelines that leverage different capabilities of the data platform, such as connectivity and data engines for processing. There are many data processing pipelines. One may: “Integrate” data from multiple sources Perform data quality checks or standardize data Apply data security-related transformations, which include masking, anonymizing, or encryption Match, merge, master, and do entity resolution Share data with partners and customers in the required format, such as HL7 Consumers or “targets” of data pipelines may include: Data warehouses like ...

Innovation has always been part of the Amazon DNA, but about 20 years ago, we went through a radical transformation with the goal of making our iterative process—"invent, launch, reinvent, relaunch, start over, rinse, repeat, again and again"—even faster. The changes we made affected both how we built applications and how we organized our company. Back then, we had only a small fraction of the number of customers that Amazon serves today. Still, we knew that if we wanted to expand the products and services we offered, we had to change the way we approached application architecture. The giant, monolithic "bookstore" application and giant database that we used to power Amazon.com limited our speed and agility. Whenever we wanted to add a new feature or product for our customers, like video streaming, we had to edit and rewrite vast amounts of code on an application that we'd designed specifically for our first product—the bookstore. This was a long, unwieldy p...

Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product. Becoming a data-driven organization remains one of the top strategic goals of many companies I work with. My clients are well aware of the benefits of becoming intelligently empowered: providing the best customer experience based on data and hyper-personalization; reducing operational costs and time through data-driven optimi...

Lyft was a late entrant to the ride-sharing business model, at least compared to its competitor Uber, which pioneered the concept and remains the largest provider. That delay in starting out actually gave Lyft a bit of an advantage in terms of architecting its big data infrastructure in the cloud, as it was able to sidestep some of the challenges that Uber faced in building out its on-prem system. Lyft and Uber, like many of the young Silicon Valley companies shaking up established business models, aren’t shy about sharing information about their computer infrastructure. They both share an ethos of openness in regards to using and developing technology. That openness is also pervasive at Google, Facebook, Twitter, and other Valley outfits that created much of the big data ecosystem, most of which is, of course, open source. So when the folks at Lyft were blueprinting how to construct a system that could do all the things that a ride-sharing app has to do – tracking and connectin...

Survey results reveal the path organizations face as they integrate cloud native infrastructure and harness the full power of the cloud. Driven by the need for agility, scaling, and resiliency, organizations have spent more than a decade moving from “trying out the cloud” to a deeper, more sustained commitment to the cloud, including adopting cloud native infrastructure. This shift is an important part of a trend we call the Next Architecture, with organizations embracing the combination of cloud, containers, orchestration, and microservices to meet customer expectations for availability, features, and performance. To learn more about the motivations and challenges companies face adopting cloud native infrastructure, we conducted a survey of 590 practitioners, managers, and CxOs from across the globe.[1] Key findings from the survey include: Nearly 50% of respondents cited lack of skills as the top challenge their organizations face in adopting cloud native infrastructure. ...

In terms of performance tuning in Snowflake, there are very few options available. However, it is worth understanding how the Snowflake architecture includes various levels of caching to help speed your queries. This article provides an overview of the techniques used, and some best practice tips on how to maximise system performance using caching. Snowflake Database Architecture Before starting it’s worth considering the underlying Snowflake architecture, and explaining when Snowflake caches data. The diagram below illustrates the overall architecture which consists of three layers:- Service Layer:   Which accepts SQL requests from users, coordinates queries, managing transactions and results.  Logically, this can be assumed to hold the  result cache  – a cached copy of the results of every query executed. Compute Layer:   Which actually does the heavy lifting.  ...

Netflix Data Landscape Freedom & Responsibility (F&R) is the lynchpin of Netflix’s culture empowering teams to move fast to deliver on innovation and operate with freedom to satisfy their mission. Central engineering teams provide paved paths (secure, vetted and supported options) and guard rails to help reduce variance in choices available for tools and technologies to support the development of scalable technical architectures. Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management. Therefore, building a complete and accurate data lineage system to map out all the data-artifacts (including in-motion and at-rest data repositories, Kafka topics, apps, reports and dashboards, interactive and ad-hoc analysis queries, ML and experimentation models) is a monumental task and requires a scalable architecture, robust design, a strong engineering team and above all, amazing cross-f...

Use Cases When we are in the outdoors, many of us often feel the need for a camera- that is intelligent enough to follow us, adjust to the terrain heights and visually navigate through the obstacles, while capturing panoramic videos.  Here, I am talking about autonomous self-flying drones, very similar to cars on auto pilot. The difference is that we are starting to see proliferation of artificial intelligence into affordable, everyday use cases, compared to relatively expensive cars. These new use cases mean: (1) They will need parallel compute processing to crunch through insane amount of data (visual or otherwise) in real time for inferences and training of deep learning neural network algorithms. This helps them distinguish between objects and get better with more data. Think like a leap of compute processing by 100x, due to the real time nature of the use cases (2) They will need the deep learning software frameworks, so that data scientists & data engi...

The Apache Kafka distributed streaming platform features an architecture that – ironically, given the name – provides application messaging that is markedly clearer and less Kafkaesque when compared with alternatives. In this article, we’ll take a detailed look at how Kafka’s architecture accomplishes this. https://insidebigdata.com/2018/04/12/developing-deeper-understanding-apache-kafka-architecture/

Big data (also called NoSQL) technologies facilitate the ingestion, processing, and search of data with no regard to schema (database structure). Web technologies such as Google, LinkedIn, and Facebook use big data technologies to process the tremendous amount of data from every possible source without regard to structure, and offer a searchable interface to access it. Modern NoSQL technologies have evolved to offer capabilities to govern, process, secure, and deliver data, and have facilitated the development of an integration pattern called the operational data hub (ODH). The Centers for Medicare and Medicaid Services (CMS) and other organizations (public and private) in the health, finance, banking, entertainment, insurance, and defense sectors (amongst others) utilize the capabilities of ODH technologies for enterprise data integration. This gives them the ability to access, integrate, master, process, and deliver data across the enterprise. Traditional mode...

Cluster schedulers are an important component of modern infrastructure, and have evolved significantly in the last few years. Their architecture has moved from monolithic designs to much more flexible, disaggregated and distributed designs. However, many current open-source offerings are either still monolithic, or otherwise lack key features. These features matter to real-world users, as they are required to achieve good utilization. Scheduling is an important topic because it directly affects the cost of operating a cluster: a poor scheduler results in low utilization, which costs money as expensive machines are left idle. High utilization, however, is not sufficient on its own: antagonistic workloads interfere with other workloads unless the decisions are made carefully. Details: http://www.firmament.io/blog/scheduler-architectures.html