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Showing posts from 2018

Getting Started with Cloudera Data Science Workbench

Last week, Cloudera announced the General Availability release of Cloudera Data Science Workbench. In this post, I'll give a brief overview of its capabilities and architecture, along with a quick-start guide to connecting Cloudera Data Science Workbench to your existing CDH cluster in three simple steps. At its core, Cloudera Data Science Workbench enables self-service data science for the enterprise. Data scientists can build, scale, and deploy data science and machine learning solutions in a fraction of the time, Read more The post Getting Started with Cloudera Data Science Workbench appeared first on Cloudera Engineering Blog.
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New in Cloudera Enterprise 5.11: Hue Data Search and Tagging

Self-service business intelligence and exploratory analytics continue to be a primary use case for Cloudera's customers. Over the past year, we have made a number of significant advancements in Hue, the intelligent SQL editor, to provide a more powerful user experience for SQL developers and make them even more productive for those use cases. The recent release of Cloudera 5.11 furthers this effort with new enhancements around embedded search and tagging for faster data discovery, Read more The post New in Cloudera Enterprise 5.11: Hue Data Search and Tagging appeared first on Cloudera Engineering Blog.
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How-to: Backup and disaster recovery for Apache Solr (part I)

Cloudera Search (that is Apache Solr integrated with the Apache Hadoop eco-system) now supports (as of C5.9) a backup and disaster recovery capability for Solr collections. In this post we will cover the basics of the backup and disaster recovery capability in Solr and hence in Cloudera Search. In the next post we will cover the design of the Solr snapshots functionality and its integration with the Hadoop ecosystem as well as public cloud platforms (e.g. Read more The post How-to: Backup and disaster recovery for Apache Solr (part I) appeared first on Cloudera Engineering Blog.
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Create conda recipe to use C extended Python library on PySpark cluster with Cloudera Data Science Workbench

Cloudera Data Science Workbench provides data scientists with secure access to enterprise data with Python, R, and Scala. In the previous article, we introduced how to use your favorite Python libraries on an Apache Spark cluster with PySpark. In Python world, data scientists often want to use Python libraries, such as XGBoost, which includes C/C++ extension. This post shows how to solve this problem creating a conda recipe with C extension. Read more The post Create conda recipe to use C extended Python library on PySpark cluster with Cloudera Data Science Workbench appeared first on Cloudera Engineering Blog.
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Scrum and eXtreme Programming (XP)

Hello, great people of the world. Welcome back to the Professional Scrum Developer (PSD) blog series with yours truly. It's been a while since my last blog on "Scrum And ...". This time I am going to discuss Scrum and eXtreme Programming (XP). I would like to discuss Scrum and XP because I often get the question, "When should I use Scrum or XP?" from people in the community. My natural answer to this question is that a professional Scrum team will use XP practices. Scrum teams who are religious in doing retrospectives will discover that they need to do XP practices if they want to go full steam in the future Sprints. You may also have heard on the street that "XP is Scrum with technical practices." Well, sort of. XP is not only about technical practices. Just like Scrum, XP is also about mindset and behaviors. We'll see why XP is not only about technical practices in this blog. In this blog, I will fit XP into Scrum, and I will be using is the Scr…

Create Microservice Pipelines With StreamSets Data Collector

A microservice is a lightweight component that implements a relatively small component of a larger system — for example, providing access to user data. A microservice architecture comprises a set of independent microservices, often implemented as RESTful web services communicating via JSON over HTTP, that together implement a system's functionality, rather than a single monolithic application. Think of an e-commerce website: we might have separate microservices for searching for inventory, managing the shopping cart, and recommending items based on the shopping cart's content. Compared to monolithic applications, the microservice approach promotes fine-grained modularity, allowing agile implementation of components by independent teams, which may even be using different technologies. Now, one of those technologies can be StreamSets Data Collector. Data Collector 3.4.0, released earlier this week, introduces microservice pipelines, with a new REST Service origin and Send Respon…

IoT Innovation in Transportation

Every day, humans are innovating various technologies that can help streamline efforts towards various operations with ease. Each passing day provides us with the opportunity to merge a new technology into our routine so that we can make the most out of it in a systematic manner. Today, there are technologies that involve machines and devices for greater insight into our day-to-day activity so that we can improve our lifestyle by upgrading our processes. One such innovation is the Internet of Things (IoT). IoT has changed the way data is collected by devices and is processed quickly for faster decision-making that minimizes the time taken to complete a certain process. IoT is driving almost every industry by revolutionizing their concepts and the way they approach a certain activity. Here, we are going to talk about IoT in transportation, because, since the introduction of IoT, we have already seen multiple benefits of this technology. Logistics in transportation is very important so …

QA for Machine Learning Models With the PDCA Cycle

The primary goal of establishing and implementing Quality Assurance (QA) practices for machine learning/data science projects or projects using machine learning models is to achieve consistent and sustained improvements in business processes, making use of underlying ML predictions. This is where the idea of the PDCA cycle (Plan-Do-Check-Act) is applied to establish a repeatable process ensuring that high-quality machine learning (ML)-based solutions are served to the clients in a consistent and sustained manner. The following diagram represents the details:
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IoT Visual Programming Tools

We all constantly hear talks about the impact that IoT is having and will have in different areas of our lives and in the production systems. But, what is IoT? Simply speaking, it is a world of connected objects that talk to each other to exchange information. Going a little deeper into the IoT ecosystem, we can identify several elements that usually build an IoT system. There are not only smart objects, but there are also gateways, sensors, and IoT cloud platforms that enable us to create dashboards and analyze the data. The interesting aspects of the Internet of Things are the possibility to explore this world by ourselves. Lately, we are noticing the birth of several IoT development boards that have different features. Starting with Arduino and Raspberry to ESP8266, Particle, NXP, and, lately, Android Things, all these development boards help us to take the first steps towards an IoT ecosystem. Moreover, the explosion of the prototyping kits that brings together IoT boards, sensors…

Building a Web App Using Spring Boot, Angular 6, and Maven

Combining Spring Boot and Angular using Maven can be a challenging task. In this post, we will create a simple web app using Spring Boot and Angular 6 and package them together in a war file. Creating a Maven Project First, create a Maven project containing two modules: one for backend and another for front-end.
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Steps for Creating Serverless Microservices With AWS Lambda and Node.js

In serverless architecture, the server will not run all the time. The server will be active only when a request is there for processing, and the rest of the time, the server will be in sleep mode. Unlike server architecture, a monthly subscription plan need not be taken, as the user only has to pay for the server's active time, which helps to reduce the server cost. Microservice architecture is a method of developing a system in small modules which communicate internally with each other through a lightweight mechanism like HTTP/REST with JSON.
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iOS Snapshot Testing

Writing user interface tests is always a bit complicated, and many developers end up leaving views without testing or dedicating a lot of time and effort to the development of tests. Apple has its own native support for UI testing but does not support view-based testing. This lack of functionality makes many people skip the part of writing tests. Therefore, we at Apiumhub decided to write an article about iOS snapshot testing. View-Based Testing View-based testing means verifying that what the user sees is exactly what we (as developers) want them to see. Thanks to this testing, we can guarantee that in different states or versions, our views are shown as expected.
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Scaling Microservices: The Challenges and Solutions

The increasingly popular microservices model — by which software solutions are comprised of multiple modular components — is a very powerful and increasingly popular one. Not only does it allow for solutions to be formulated from the best individual components available, it also allows for the most effective services to be redeployed in different combinations. In this way, microservices represent a much more versatile solution to many common computing challenges. But while microservices can be combined in an endless array of combinations in order to tackle a variety of problems, the concept and architecture present a series of unique challenges when it comes to scalability. That's not to say that microservices are not scalable, they are considered more scalable than their preceding monolithic legacy applications, but they do require a very different approach to scaling.
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Perfect smooth scrolling in UITableViews

I have some years of experience in development for the best mobile platform — iOS. During this time I've seen many iOS apps and many iOS engineers.Our world is full of good developers, but sometimes I notice that some of them have no idea how to use the whole potential of one of the most popular mobile devices on the planet for making really smooth applications.And now I'll try to explain my vision of the optimizations an engineer should use to make UITableViews faster and smooth.Difficulty and the depth of material will increase from the beginning to the end of story, so I'll start with things which are familiar to many of you. Deeper aspects of the iOS drawing system and UIKit will be covered at the end.Built-In ToolsI really believe that most people reading this story know about this, but some people, even after using these tools, are not using it in the right way.The first one is reusing single instance of cell/header/footer even if we need to show more. This is most o…

Agile Principles in Action

I recently wrote about the Agile Values and what they mean to me. So many people I run into think just using a set of specific practices will make their teams Agile – wrong. Over the years I've come to realize that Agile is mostly a mindset – a way of thinking differently about software development. Incorporating sprints, user stories, daily stand-ups, and other process-related practices can still lead to failure – unless we also change the way we think. The simplicity of Agile – the Agile Manifesto's four values and twelve principles – can sometimes mask the challenge of learning to think and behave differently. A culture of agility means embracing ideas that many of us are uncomfortable with, or even fear.
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Angular Libraries and Microservices

We live in a Microservices world, and this world is here to stay. Back-end developers need to dive into Domain Driven Design, write stateless, resilient, highly available services, keep data synchronized through Change Data Capture, handle network failure, deal with authentication, authorization, JWT... and expose a beautiful Hypermedia API so the front-end developers can add a great user interface to it. Good! But what about the front-end?
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Continuous Integration and Continuous Delivery for Database Changes

Introduction Over the last two decades, many application development teams have adopted Agile development practices and have immensely benefited from it. Delivering working software frequently, receiving early feedback from customers and having self-organized cross-functional teams led to faster delivery to market and satisfied customers. Another software engineering culture, called DevOps, started evolving towards the middle of this decade and is now popular among many organizations. DevOps aims at unifying software development (Dev) and IT operations (Ops). Both these software engineering practices advocate automation, and two main concepts coming out of them are Continuous Integration (CI) and Continuous Delivery (CD). The purpose of this article is to highlight how Database Change Management, which is an important aspect of the software delivery process, is often the bottleneck in implementing a Continuous Delivery process. The article also recommends some processes that help in o…

Python Top 10 Articles for the Past Month (v.Aug 2018)

For the past month, we ranked nearly 1,000 Python articles to pick the Top 10 stories that can help advance your career (1% chance).body[data-twttr-rendered="true"] {background-color: transparent;}.twitter-tweet {margin: auto !important;}Python is becoming the world's most popular coding language https://t.co/wCIL4dQLqd — @TheEconomistfunction notifyResize(height) {height = height ? height : document.documentElement.offsetHeight; var resized = false; if (window.donkey && donkey.resize) {donkey.resize(height); resized = true;}if (parent && parent._resizeIframe) {var obj = {iframe: window.frameElement, height: height}; parent._resizeIframe(obj); resized = true;}if (window.location && window.location.hash === "#amp=1" && window.parent && window.parent.postMessage) {window.parent.postMessage({sentinel: "amp", type: "embed-size", height: height}, "*");}if (window.webkit && window.webkit.me…

Daily Scrum: Top 5 Myths

The Daily Scrum is an important inspect and adapt event for the Development Team. The Development Team devises its day plan during the Daily Scrum, what needs to be done to accomplish/make progress towards the Sprint goal. As much important as the Daily Scrum is, there are as many myths and misconceptions associated with it. Here are the top 5 myths that I have observed in my experience.
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Twitter is purging accounts that were trying to evade prior suspensions

Twitter announced this afternoon it will begin booting accounts off its service from those who have tried to evade their account suspension. The company says that the accounts in question are users who have been previously suspended on Twitter for their abusive behavior, or for trying to evade a prior suspension. These bad actors have been able to work around Twitter's attempt to remove them by setting up another account, it seems. The company says the new wave of suspensions will hit this week and will continue in the weeks ahead, as it's able to identify others who are "attempting to Tweet following an account suspension." This week, we are suspending accounts for attempting to evade an account suspension. These accounts were previously suspended for abusive behavior or evading a previous suspension, and are not allowed to continue using Twitter. — Twitter Safety (@TwitterSafety) August 14, 2018 Twitter's announcement on the matter – which came in the form of a…

20 Best Practices for Working With Apache Kafka at Scale

Apache Kafka is a widely popular distributed streaming platform that thousands of companies like New Relic, Uber, and Square use to build scalable, high-throughput, and reliable real-time streaming systems. For example, the production Kafka cluster at New Relic processes more than 15 million messages per second for an aggregate data rate approaching 1 Tbps. Kafka has gained popularity with application developers and data management experts because it greatly simplifies working with data streams. But Kafka can get complex at scale. A high-throughput publish-subscribe (pub/sub) pattern with automated data retention limits doesn't do you much good if your consumers are unable to keep up with your data stream and messages disappear before they're ever seen. Likewise, you won't get much sleep if the systems hosting the data stream can't scale to meet demand or are otherwise unreliable.
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Building a Dating Site With Neo4j (Part 1)

You might have already heard that Facebook is getting into the Dating business. Other dating sites have been using graphs in the past and we've looked at finding love using the graph before. It has been a while though, so let's return to the topic making use of the new Date and Geospatial capabilities of Neo4j 3.4. I have to warn you though that I've been with Helene for almost 15 years and missed out on all this dating site fun, what I do know I blame Colin for it and some pointers from the comments section of this blog post. Dating sites face a series of challenges, the first one is lack of users. Only two ways to fix that, the first one involves having lots of money to pay for national advertisements, the second involves word of mouth. So you dear reader have to either invest a few million dollars or join our new dating site and tell all your friends about it.
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Building a Dating Site With Neo4j (Part 2)

We came up with an idea for a dating site and an initial model in Part One. Next, we are going to work on a back end HTTP API, because I'm old school and that's the way I like it. We will build our HTTP API right into Neo4j using an extension which turns Neo4j from a Server into a Service. Unlike last time where we wrote a clone of Twitter, I don't really know where I'm going with this, so let's start with some of the obvious API endpoints and then we can design and build more as we go along. Is this Agile or am I just being an idiot? I can't tell, so onward we go. First obvious thing is, we need a schema. Luckily Neo4j is a "Schema Optional" database so we don't have to worry about designing any tables or properties or figuring out what kind of properties each table will have. Because… well, we don't have tables. The only real schema we need to worry about are Constraints and Indexes. For example, we don't want two users to have the same …

Jump to Start Test-Driven Database Development (TDDD) – Part 2

We discussed the basics of test-driven database development (TDDD) with examples and compared it with traditional database development in the first part of this article. In the second part, we are going to move beyond basics to focus on a more realistic scenario of meeting report requirements by using TDDD. A quick recap of TDDD at this point is handy in understanding how it can help us to achieve such goals. Test-driven database development (TDDD) Recap As the name indicates, test-driven database development (TDDD) is a database development method of meeting requirements through unit tests. It is always necessary to implement unit tests before the main database code since they drive the database development as a whole. In other words, database objects (such as "view", "stored procedure", etc.) are necessary to meet the database-related business requirements but they always have to pass the unit test(s) before they are enough appropriate for such purpose. In TDDD, …

SQL Server IntelliSense and Autocomplete

Starting from SQL Server 2008, Microsoft introduced a new feature in the SQL Server Management Studio that helps the database developers and the database administrators writing the T-SQL commands faster by reducing the typing effort and providing a quick access to the syntax information via listing all available database objects with their properties. This feature is called IntelliSense. IntelliSense in SQL Server Management Studio Microsoft IntelliSense allows you to find the necessary information while writing the T-SQL script without leaving the query editor for such purposes. It's just needed to write the T-SQL script and the IntelliSense will complete the typing automatically with providing all available information. Microsoft IntelliSense is enabled as default in SQL Server Management Studio. If it is disabled, you can easily enable it from the SQL Server Management Studio by choosing Options from the Tools task menu as shown below: Browse for the IntelliSense settings windo…

Structured Query Language – Importance of learning SQL

Computer programming language is a set of detailed instructions for computers or machines for performing specific actions. Through a programming language, we can control the behavior and output of a computer via accurate algorithms. A programming language is also called a computer language or programming system. The computer performs with various programming languages, such as SQL, Java, C++, Python, etc.. These languages allow computers and machines processing the large and complex data more effectively and quickly. That is the reason we find them valuable to organizations, particularly in the creation of system database management services. What is Structured Query Language? SQL stands for "Structured Query Language". Raymond Boyce and Donald Chamberlin developed SQL at IBM in the early 1970s. It was created for getting access and modifying data held in databases. Initially, it was called SEQUEL (Structured English Query Language) but later needed to change its name becaus…

Python vs. R: Which Should You Choose For Your Next ML Project?

Data science is all about capturing data in an insightful way, whereas Machine Learning is a key area of it. Data science is a fantastic blend of advanced statistics, problem-solving, mathematics expertise, data inference, business acumen, algorithm development, and real-world programming ability. And Machine Learning is a set of algorithms that enable software applications to become more precise in predicting outcomes or take actions to separate it without being explicitly programmed. The distinction between data science and Machine Learning is a bit fluid, but the main idea is that data science emphasizes statistical inference and interpretability, while Machine Learning prioritizes predictive accuracy over model interpretability. And for both data science and Machine Learning, open source has become almost the de facto license for innovative new tools.
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Multi-Document Transactions on MongoDB 4.0

At the end of last month, MongoDB World announced the release of MongoDB 4.0, which supports multi-document transactions against replica sets. Alibaba Cloud ApsaraDB Database R&D engineers first analyzed the source code of transaction functions and parsed the transaction implementation mechanism. In this article, we will discuss the implementation of multi-document transactions on MongoDB 4.0. What's New on MongoDB 4.0 The transaction functionality introduced by MongoDB 4.0 supports multi-document ACID features such as transaction operations using the mongo shell.
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5 TensorFlow and ML Courses for Programmers

If you are curious about Artificial Intelligence, Data Science, and Machine Learning, then I am sure you have heard about TensorFlow, Google's Machine Learning API, which they have used to develop the RankBrain algorithm for Google Search. TensorFlow is one of the most popular Machine Learning APIs, which allows you to automate several real-world tasks. For example, you can use it for image detection. In fact, a Japanese farmer turned programmer used it to detect the best quality cucumber, which only his father and mother can do even with his years of experience. That's the power of Machine Learning, which is now available to everyone because of TensorFlow. If you have not read that story, I strongly suggest you read, in fact, that's one of the few stories that motivated me to learn more about Machine Learning. One of my goals in 2018 is to explore Machine Learning and Data Science and that's why I am learning Python as well. You might have seen a couple of python post…

Export data from SQL Server to Excel and Text file via using SSIS package

While using the SSIS data flow, we can perform ETL task that can be used for data migration, data offloading, design and implementation of the data warehouse. In this article, I explain how to export data from an SQL table to excel and text file by using the SSIS data flow task. In this demo, I will perform through the following steps: 1. Create an SSIS package and create ADO.Net connection manager. 2. Create data flow task that has ADO.NET source to populate data from SQL Server. Data will be written to excel file and text file hence configure flat file destination and excel destination. 3. Execute SSIS package and verify that data was written to excel and text files. Create SSIS package and ADO.NET connection For this demo, I used SQL Server Data tools 2015. Firstly, I am creating a new Integration service project named "Export SQL Data": After the creation of an SSIS project, I will create the data flow task. Data flow task will use SQL Server as a source. Excel file and …

7 Key Factors to Consider Before Choosing a Chatbot Platform

Artificial Intelligence and Machine Learning — The two words that are on the tip of every technophile's tongue. It is fair to say that, over the years, AI and ML have successfully transformed almost every aspect of our personal and, more importantly, our professional lives. Research says that almost 85% of global executives believe that AI will allow their companies to obtain or sustain a competitive advantage and is going to play a much larger role in enhancing the organizational work productivity.
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Python ODBC Custom API

This is a continuation of my previous article, "Database Connectivity and Transaction in Python." I strongly recommend you to read the above article first. Here, I am going to explain how to design your own generic ODBC API class in python through which you can connect to Oracle, MS SQL server, Sybase, MySql, etc. Also, I have added one more API through which you can access columns of a table with their respective column name. Let's start:
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Jump to Start Test-Driven Database Development (TDDD) – Part 3

This article is a walk-through of creating a report base on a database object developed and tested by using test-driven development (TDDD). Furthermore, some tips for improving database unit testing via TDDD will be discussed in this article too. Test-driven database development (TDDD) Recap In simple words, TDDD is all about writing unit-test before the database object is even created. So, if we want to create a database object to satisfy some business requirement, it should be processed by creating a unit-test that ensures the object exists. Moreover, it has to be followed by another unit-test that will ensure the proper functioning of the database object (at the minimum) though that functioning check should only be limited to meet only the desired requirement. Jump to Start TDDD Part-1 Recap In the first part of the article, traditional database development was compared with test-driven database development (TDDD) and provided with coding examples. TDDD began with the unit-test aim…

Cassandra Cluster Install on Ubuntu 18.04 for Big Data

In this post, I'm going to install a complete 'production ready' Apache Cassandra cluster of three nodes. This post is an expansion on a previous post, Install Cassandra on Ubuntu 18.04 where I setup and configured a single Cassandra server. The problem is that it wasn't production ready and couldn't hold large volumes of data. I wanted to store big data using Cassandra and these means data sized 1 TB. So this cluster will consist of three nodes each with 500 GB of space available for storage.
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7 Bad Habits to Avoid as a QA Engineer

Most people would agree that software quality is important. We have seen the results of buggy software in all kinds of situations: from Mars probes malfunctioning and chemotherapy machines administering lethal doses of radiation, to telecommunications systems experiencing a cascade failure. It would seem logical to assume that software testers would be much-valued members of ...
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