Notes about Cutting-Edge Technologies and Everything

  Database management systems continue their move to the cloud — a move that is producing an increasingly complex landscape of vendors and offerings. This Magic Quadrant will help data and analytics leaders make the right choices in a complex and fast-evolving market. Strategic Planning Assumptions By 2025, cloud preference for data management will substantially reduce the vendor landscape while the growth in multicloud will increase the complexity for data governance and integration. By 2022, cloud database management system (DBMS) revenue will account for 50% of the total DBMS market revenue. These DBMSs reflect optimization strategies designed to support transactions and/or analytical processing for one or more of the following use cases:     Traditional and augmented transaction processing     Traditional and logical data warehouse     Data science exploration/deep learning     Stream/event processing   ...

  The following post focuses on AWS, MS Azure, and GCP in detail. Learn more about each cloud service and how to choose the best one for your business needs.  Digitalization is being embraced by all of us across the globe, especially cloud computing technology. Whether it's because of its scalability or security or reduced costs, cloud platforms have sprung up to a great extent over a few years. Gone are the days when businesses were confused about whether to choose a cloud service provider or not. Now the confusion surrounds the question of which cloud service provider to use. AWS, Azure, and Google Cloud are our top three contenders. Recently, I happen to stumble upon an informative post focusing on AWS Lambda vs Azure Functions. I must say this one was quite detailed and well-structured. Here they have successfully covered all the aspects that are essential and dominating while we compare lambda vs azure. And I am pretty sure considering both the posts together will act a...

  Data helps companies take the guesswork out of decision-making. Teams can use data-driven evidence to decide which products to build, which features to add, and which growth initiatives to pursue. And, such insights-driven businesses grow at an annual rate of over 30%. But, there’s a difference between being merely data-aware and insights-driven. Discovering insights requires finding a way to analyze data in near real-time, which is where cloud data warehouses play a vital role. As scalable repositories of data, warehouses allow businesses to find insights by storing and analyzing huge amounts of structured and semi-structured data. And, running a data warehouse is more than a technical initiative. It’s vital to the overall business strategy and can inform an array of future product, marketing, and engineering decisions. But, choosing a cloud data warehouse provider can be challenging. Users have to evaluate costs, performance, the ability to handle real-time workloads, and other...

  One of the challenges with cloud migration is that you’re solving a puzzle with multiple pieces. In addition to a number of workloads you could migrate, you’re also solving for challenges you’re facing, the use cases driving you to migrate, and the benefits you’re looking to gain. Each organization’s puzzle will likely get solved in their own unique way, but thankfully there is plenty of guidance on how you can migrate common workloads in successful ways.  In addition to working directly with our Rapid Assessment and Migration Program (RAMP), we also offer a plethora of self-service guides to help you succeed! Some of these guides, which we’ll cover below, are designed to help you identify the best ways to migrate, which include meeting common organizational goals like minimizing time and risk during your migration, identifying the most enterprise-grade infrastructure for your workloads, picking a cloud that aligns with your organization’s sustainability goals...

Serverless computing is redefining the way organizations develop, deploy, and integrate cloud-native applications. According to an industry report, market size of serverless computing is expected to reach 7.72 billion by 2021. A new and compelling paradigm for the deployment of cloud applications, serverless computing is at the precipice of enterprise shift towards containers and microservices. In the year 2021, serverless paradigm shift presents exciting opportunities to organizations by providing a simplified programming model for creating cloud applications by abstracting away most operational concerns. Major cloud vendors, Microsoft, Google, and Amazon are already in the game with their respective offering and there is no reason you shouldn’t aboard the train. 2021 is the year of FaaS All major providers of serverless computing offer several types and tiers of database and storage services to their customers. In addition, all major cloud player such as Amazon, Microsoft and Google ...

Microsoft Azure and Google Cloud have their advantages, but they don’t match the breadth and depth of the Amazon cloud. The reason is simple: AWS has built out so many products and services that it’s impossible to begin to discuss them in a single article or even a book. Many of them were amazing innovations when they first appeared and the hits keep coming. Every year Amazon adds new tools that make it harder and harder to justify keeping those old boxes pumping out heat and overstressing the air conditioner in the server room down the hall. For all of its dominance, though, Amazon has strong competitors. Companies like Microsoft, Google, IBM, Oracle, SAP, Rackspace, Linnode, and Digital Ocean know that they must establish a real presence in the cloud and they are finding clever ways to compete and excel in what is less and less a commodity business. These rivals offer great products with different and sometimes better approaches. In many cases, they’re running neck and neck wi...

Diagnosing the problem In 2016, as we started migrating to the Google Cloud Platform, we saw an explosion of dataset creation in BigQuery. At this time, we also drastically increased our hiring of insights specialists (data scientists, analysts, user researchers, etc.) at Spotify, resulting in more research and insights being produced across the company. However, research would often only have a localized impact in certain parts of the business, going unseen by others that might find it useful to influence their decision making. Datasets lacked clear ownership or documentation making it difficult for data scientists to find them. We believed that the crux of the problem was that we lacked a centralized catalog of these data and insights resources. In early 2017, we released Lexikon, a library for data and insights, as the solution to this problem. The first release allowed users to search and browse available BigQuery tables (i.e. datasets)— as well as discover k...

In order to serve customers across 19+ products, GOJEK places a lot of emphasis on data. Our Data Warehouse, built by integrating data from multiple applications and sources, helps our team of data scientists, as well as business and product analysts make solid, data-driven decisions. This post explains our open source solution for easy movement of data from Kafka to BigQuery. Data Warehouse setup at GOJEK. We use Google Bigquery (BQ) as our Data Warehouse, which serves as a powerful tool for interactive analysis. This has proven extremely valuable for our use cases. Our approach to push data to our warehouse is to first push the data to Kafka. We rely on multiple Kafka clusters to ingest relevant events across teams. A common approach to push data from Kafka to BigQuery is to first push it to GCS, and then import said data into BigQuery from GCS. While this solves the use case of running analytics on historical data, we also use BigQuery for near-real-time analytics & r...

Here’s a curated list of resources for data engineers, with sections for algorithms and data structures, SQL, databases, programming, tools, distributed systems, and more. Useful articles The AI Hierarchy of Needs The Rise of Data Engineer The Downfall of the Data Engineer A Beginner’s Guide to Data Engineering Part I Part II Part III Functional Data Engineering — a modern paradigm for batch data processing How to become a Data Engineer (in Russian) Talks Data Engineering Principles - Build frameworks not pipelines by Gatis Seja Functional Data Engineering - A Set of Best Practices by Maxime Beauchemin Advanced Data Engineering Patterns with Apache Airflow by Maxime Beauchemin Creating a Data Engineering Culture by Jesse Anderson Algorithms & Data Structures Algorithmic Toolbox in Russian Data Structures in Russian Data Structures & Algorithms Specialization on Coursera Algorithms Specialization from Stanford on Coursera SQL Com...

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. ...

Getting Started With The S3-Interop API for GCS To start the process, enable the Google Cloud Storage service in the Google Cloud console and create a project and bucket for testing. You can then enable the S3-interoperable API in the Interoperability tab within Project Settings. Google enables the S3-interoperability API on a per-user basis for each project. This means that you’ll want to ensure you have a unique credential or user account for each end-user or service if you want more meaningful access logs. While you’re in the interoperability settings, create an access key and save it locally for reference. As the Interoperability settings page describes, the secret key lets you authorize requests with HMAC authentication. You can calculate the signatures for each request manually in your shell or REPL, or use a library. Read full article >>>

The beta release of "Spark Operator" allows native execution of Spark applications on Kubernetes clusters -- no Hadoop or Mesos required. Apache Spark is a hugely popular execution framework for running data engineering and machine learning workloads. It powers the Databricks platform and is available in both on-premises and cloud-based Hadoop services, like Azure HDInsight, Amazon EMR and Google Cloud Dataproc. It can run on Mesos clusters too. But what of you just want to run your Spark workloads on a Kubernetres (k8s) cluster sans Mesos, and without the Hadoop YARN strings attached? While Spark first added Kubernetes-specific features in its 2.3 release, and improved them in 2.4, getting Spark to run natively on k8s, in a fully integrated fashion, can still be a challenge. KUBE OPERATOR Today, Google, which created Kubernetes in the first place, is announcing the beta release of the Kubernetes Operator for Apache Spark -- "Spark Operator" for short. Sp...