Apache Hive 3 introduces new capabilities for data analytics including materialized views, default columns, constraints, and improved JDBC and Kafka connectors to enable real-time streaming and integration with external systems like Druid; Hive 3 also improves performance and query optimization through a new query result cache, workload management, and cloud storage optimizations. Data Analytics Studio provides self-service analytics on top of Hive 3 through a visual interface to optimize queries, monitor performance, and manage data lifecycles.
This document provides an overview of Hive and its performance capabilities. It discusses Hive's SQL interface for querying large datasets stored in Hadoop, its architecture which compiles SQL queries into MapReduce jobs, and its support for SQL semantics and datatypes. The document also covers techniques for optimizing Hive performance, including data abstractions like partitions, buckets and skews. It describes different join strategies in Hive like shuffle joins, broadcast joins and sort-merge bucket joins and how they are implemented in MapReduce. The overall presentation aims to explain how Hive provides scalable SQL processing for big data.
The document compares the query execution plans produced by Apache Hive and PostgreSQL. It shows that Hive's old-style execution plans are overly verbose and difficult to understand, providing many low-level details across multiple stages. In contrast, PostgreSQL's plans are more concise and readable, showing the logical query plan in a top-down manner with actual table names and fewer lines of text. The document advocates for Hive to adopt a simpler execution plan format similar to PostgreSQL's.
This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.
Efficient Data Storage for Analytics with Apache Parquet 2.0Cloudera, Inc.
Apache Parquet is an open-source columnar storage format for efficient data storage and analytics. It provides efficient compression and encoding techniques that enable fast scans and queries of large datasets. Parquet 2.0 improves on these efficiencies through enhancements like delta encoding, binary packing designed for CPU efficiency, and predicate pushdown using statistics. Benchmark results show Parquet provides much better compression and query performance than row-oriented formats on big data workloads. The project is developed as an open-source community with contributions from many organizations.
Tez is the next generation Hadoop Query Processing framework written on top of YARN. Computation topologies in higher level languages like Pig/Hive can be naturally expressed in the new graph dataflow model exposed by Tez. Multi-stage queries can be expressed as a single Tez job resulting in lower latency for short queries and improved throughput for large scale queries. MapReduce has been the workhorse for Hadoop but its monolithic structure had made innovation slower. YARN separates resource management from application logic and thus enables the creation of Tez, a more flexible and generic new framework for data processing for the benefit of the entire Hadoop query ecosystem.
Druid and Hive Together : Use Cases and Best PracticesDataWorks Summit
Two popular open source technologies, Druid and Apache Hive, are often mentioned as viable solutions for large-scale analytics. Hive works well for storing large volumes of data, although not optimized for ingesting streaming data and making it available for queries in realtime. On the other hand, Druid excels at low-latency, interactive queries over streaming data and making data available in realtime for queries. Although the high level messaging presented by both projects may lead you to believe they are competing for same use case, the technologies are in fact extremely complementary solutions.
By combining the rich query capabilities of Hive with the powerful realtime streaming and indexing capabilities of Druid, we can build more powerful, flexible, and extremely low latency realtime streaming analytics solutions. In this talk we will discuss the motivation to combine Hive and Druid together alongwith the benefits, use cases, best practices and benchmark numbers.
The Agenda of the talk will be -
1. Motivation behind integrating Druid with Hive
2. Druid and Hive together - benefits
3. Use Cases with Demos and architecture discussion
4. Best Practices - Do's and Don'ts
5. Performance vs Cost Tradeoffs
6. SSB Benchmark Numbers
In a world where compute is paramount, it is all too easy to overlook the importance of storage and IO in the performance and optimization of Spark jobs.
Building robust CDC pipeline with Apache Hudi and DebeziumTathastu.ai
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.
Hive on Tez with LLAP (Late Loading Application) can achieve query processing speeds of over 100,000 queries per hour. Tuning various Hive and YARN parameters such as increasing the number of executor and I/O threads, memory allocation, and disabling consistent splits between LLAP daemons and data nodes was needed to reach this performance level on a test cluster of 45 nodes. Future work includes adding a web UI for monitoring LLAP clusters and implementing column-level access controls while allowing other frameworks like Spark to still access data through HiveServer2 and prevent direct access to HDFS for security reasons.
Choosing an HDFS data storage format- Avro vs. Parquet and more - StampedeCon...StampedeCon
At the StampedeCon 2015 Big Data Conference: Picking your distribution and platform is just the first decision of many you need to make in order to create a successful data ecosystem. In addition to things like replication factor and node configuration, the choice of file format can have a profound impact on cluster performance. Each of the data formats have different strengths and weaknesses, depending on how you want to store and retrieve your data. For instance, we have observed performance differences on the order of 25x between Parquet and Plain Text files for certain workloads. However, it isn’t the case that one is always better than the others.
This document discusses improvements to ORC support in Apache Spark 2.3. It describes previous issues with ORC performance and compatibility in Spark. The current approach in Spark 2.3 introduces a new native ORC file format that provides significantly better performance compared to the previous Hive ORC implementation. It allows configuring the ORC implementation and reader type. The document also demonstrates ORC usage in Spark and PySpark. Benchmark results show the native ORC reader provides up to 15x faster performance for scans and predicate pushdown. Future work items are discussed to further improve ORC support in Spark.
This document discusses Apache Ranger, an open source framework for centralized security administration across Hadoop ecosystems. It provides a presentation on securing Hadoop with Ranger, including an overview of current Hadoop security, how Ranger addresses this with centralized policy management and plugins for Hadoop components like HDFS, Hive and HBase. The document outlines Ranger's architecture and components like the policy administration server, user sync server and plugins, demonstrating how Ranger implements authorization for different Hadoop tools and integrates with their native permissions systems.
Interactive real-time dashboards on data streams using Kafka, Druid, and Supe...DataWorks Summit
When interacting with analytics dashboards, in order to achieve a smooth user experience, two major key requirements are quick response time and data freshness. To meet the requirements of creating fast interactive BI dashboards over streaming data, organizations often struggle with selecting a proper serving layer.
Cluster computing frameworks such as Hadoop or Spark work well for storing large volumes of data, although they are not optimized for making it available for queries in real time. Long query latencies also make these systems suboptimal choices for powering interactive dashboards and BI use cases.
This talk presents an open source real-time data analytics stack using Apache Kafka, Druid, and Superset. The stack combines the low-latency streaming and processing capabilities of Kafka with Druid, which enables immediate exploration and provides low-latency queries over the ingested data streams. Superset provides the visualization and dashboarding that integrates nicely with Druid. In this talk we will discuss why this architecture is well suited to interactive applications over streaming data, present an end-to-end demo of complete stack, discuss its key features, and discuss performance characteristics from real-world use cases. NISHANT BANGARWA, Software engineer, Hortonworks
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...InfluxData
The document discusses updates to InfluxDB IOx, a new columnar time series database. It covers changes and improvements to the API, CLI, query capabilities, and path to open sourcing builds. Key points include moving to gRPC for management, adding PostgreSQL string functions to queries, optimizing functions for scalar values and columns, and monitoring internal systems as the first step to releasing open source builds.
The document summarizes Apache Phoenix and its past, present, and future as a SQL interface for HBase. It describes Phoenix's architecture and key features like secondary indexes, joins, aggregations, and transactions. Recent releases added functional indexes, the Phoenix Query Server, and initial transaction support. Future plans include improvements to local indexes, integration with Calcite and Hive, and adding JSON and other SQL features. The document aims to provide an overview of Phoenix's capabilities and roadmap for building a full-featured SQL layer over HBase.
Performance Optimizations in Apache ImpalaCloudera, Inc.
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
Apache Hive is a data warehouse software built on top of Hadoop that allows users to query data stored in various databases and file systems using an SQL-like interface. It provides a way to summarize, query, and analyze large datasets stored in Hadoop distributed file system (HDFS). Hive gives SQL capabilities to analyze data without needing MapReduce programming. Users can build a data warehouse by creating Hive tables, loading data files into HDFS, and then querying and analyzing the data using HiveQL, which Hive then converts into MapReduce jobs.
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Apache Tez - A New Chapter in Hadoop Data ProcessingDataWorks Summit
Apache Tez is a framework for accelerating Hadoop query processing. It is based on expressing a computation as a dataflow graph and executing it in a highly customizable way. Tez is built on top of YARN and provides benefits like better performance, predictability, and utilization of cluster resources compared to traditional MapReduce. It allows applications to focus on business logic rather than Hadoop internals.
The document discusses new features in Apache Hive 3 including the Data Analytics Studio, connectors to other data systems like Druid and Kafka, and SQL enhancements such as materialized views, constraints and defaults, and query result caching. It provides examples of how these new capabilities can optimize workloads, improve query performance, and enable more flexible data integration and analysis.
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
The document discusses new features and enhancements in Apache Hive 3.0 including:
1. Improved transactional capabilities with ACID v2 that provide faster performance compared to previous versions while also supporting non-bucketed tables and non-ORC formats.
2. New materialized view functionality that allows queries to be rewritten to improve performance by leveraging pre-computed results stored in materialized views.
3. Enhancements to LLAP workload management that improve query scheduling and enable better sharing of resources across users.
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Speaker: Alan Gates, Co-Founder, Hortonworks
Hive 3 New Horizons DataWorks Summit Melbourne February 2019alanfgates
Hive 3 new SQL features including LLAP, workload management, SQL over Kafka and JDBC data sources, integration with Spark via Hive Warehouse Connector, ACID 2, and constraints and default values
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
The document discusses Apache Hive and Apache Druid for fast SQL on big data. It provides performance benchmarks showing Hive LLAP is faster than Presto and Spark SQL for TPC-DS queries. It describes features of Hive LLAP including in-memory caching, query result caching, and metadata caching. It also discusses new Hive 3 features like materialized views and optimizer improvements. The document then provides an overview of Apache Druid's capabilities for real-time ingestion and querying of streaming data before discussing how Hive and Druid can work together, with Hive able to push down queries to Druid.
Modernize Your Existing EDW with IBM Big SQL & Hortonworks Data PlatformHortonworks
Find out how Hortonworks and IBM help you address these challenges to enable success to optimize your existing EDW environment.
https://hortonworks.com/webinar/modernize-existing-edw-ibm-big-sql-hortonworks-data-platform/
Hortonworks Technical Workshop: What's New in HDP 2.3Hortonworks
Hortonworks Data Platform (HDP) 2.3 includes several new capabilities:
1) It improves the user experience with more guided configuration, customizable dashboards, and improved workload management.
2) It enhances security with new data encryption at rest and extends data governance.
3) It adds proactive cluster monitoring through Hortonworks SmartSense to enhance support.
Cloudera Operational DB (Apache HBase & Apache Phoenix)Timothy Spann
Cloudera Operational DB (Apache HBase & Apache Phoenix)
Using Apache NiFi 1.10 to read/write from HBase
Dec 2019, Timothy Spann, Field Engineer, Data in Motion
Princeton Meetup 10-dec-2019
https://www.meetup.com/futureofdata-princeton/events/266496424/
Hosted By PGA Fund at:
https://pga.fund/coworking-space/
Princeton Growth Accelerator
5 Independence Way, 4th Floor, Princeton, NJ
Interactive Analytics at Scale in Apache Hive Using DruidDataWorks Summit
Druid is an open-source analytics data store specially designed to execute OLAP queries on event data. Its speed, scalability and efficiency have made it a popular choice to power user-facing analytic applications, including multiple BI tools and dashboards. However, Druid does not provide important features requested by many of these applications, such as a SQL interface or support for complex operations such as joins. This talk presents our work on extending Druid indexing and querying capabilities using Apache Hive. In particular, our solution allows to index complex query results in Druid using Hive, query Druid data sources from Hive using SQL, and execute complex Hive queries on top of Druid data sources. We describe how we built an extension that brings benefits to both systems alike, leveraging Apache Calcite to overcome the challenge of transparently generating Druid JSON queries from the input Hive SQL queries. We conclude with an experimental evaluation highlighting the performant and powerful integration of these projects.
Speaker
Jesus Camancho Rodriquez, Hortonworks
Using Apache Hadoop and related technologies as a data warehouse has been an area of interest since the early days of Hadoop. In recent years Hive has made great strides towards enabling data warehousing by expanding its SQL coverage, adding transactions, and enabling sub-second queries with LLAP. But data warehousing requires more than a full powered SQL engine. Security, governance, data movement, workload management, monitoring, and user tools are required as well. These functions are being addressed by other Apache projects such as Ranger, Atlas, Falcon, Ambari, and Zeppelin. This talk will examine how these projects can be assembled to build a data warehousing solution. It will also discuss features and performance work going on in Hive and the other projects that will enable more data warehousing use cases. These include use cases like data ingestion using merge, support for OLAP cubing queries via Hive’s integration with Druid, expanded SQL coverage, replication of data between data warehouses, advanced access control options, data discovery, and user tools to manage, monitor, and query the warehouse.
Hp Converged Systems and Hortonworks - Webinar SlidesHortonworks
Our experts will walk you through some key design considerations when deploying a Hadoop cluster in production. We'll also share practical best practices around HP and Hortonworks Data Platform to get you started on building your modern data architecture.
Learn how to:
- Leverage best practices for deployment
- Choose a deployment model
- Design your Hadoop cluster
- Build a Modern Data Architecture and vision for the Data Lake
Sharing metadata across the data lake and streamsDataWorks Summit
Traditionally systems have stored and managed their own metadata, just as they traditionally stored and managed their own data. A revolutionary feature of big data tools such as Apache Hadoop and Apache Kafka is the ability to store all data together, where users can bring the tools of their choice to process it.
Apache Hive's metastore can be used to share the metadata in the same way. It is already used by many SQL and SQL-like systems beyond Hive (e.g. Apache Spark, Presto, Apache Impala, and via HCatalog, Apache Pig). As data processing changes from only data in the cluster to include data in streams, the metastore needs to expand and grow to meet these use cases as well. There is work going on in the Hive community to separate out the metastore, so it can continue to serve Hive but also be used by a more diverse set of tools. This talk will discuss that work, with particular focus on adding support for storing schemas for Kafka messages.
Speaker
Alan Gates, Co-Founder, Hortonworks
Bring Strategic Portfolio Management to Monday.com using OnePlan - Webinar 18...OnePlan Solutions
Unlock the full potential of your projects with OnePlan’s seamless integration with monday.com. Join us to discover how OnePlan enhances monday.com by aligning your portfolio of projects with your organization’s strategic goals, optimizing resource allocation, and streamlining performance tracking. Learn how this powerful combination can drive efficiency, cost savings, and strategic success within your organization.
CrushFTP 10.4.0.29 PC Software - WhizNewsEman Nisar
Introduction:
In this never-ending digital world, the essence of a smooth and safe file transfer solution is vital. CrushFTP 10.4.0.29 is a kind of full-featured, robust, and easy-to-use PC software designed for a smooth file transfer process without compromising security. In this review, we will dig in deep regarding the CrushFTP features, functions, and system requirements to have a 360-degree view of its capabilities and possible applications.
Description:
CrushFTP, LLC develop the software, and it comes in a bundle of new features and improvements, which are set to deliver a great experience to the user.With CrushFTP, from the smallest to the most extensive scale of businesses, all kinds of file transfer operations can be centrally managed on a single platform.
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Abstract:
At its heart, CrushFTP is a powerful server that allows users to exchange files over the networks safely. Many features of the FTP servers have been extended in CrushFTP. It supports protocols like FTPS, SFTP, SCP, HTTP, and HTTPS for maximum flexibility with client applications and devices.
The intuitive web interface enables users to use file management tools simply without installing complex client software.
Software Characteristics:
Security:
CrushFTP ensures security through the use of protocols for encryption, such as SSL/TLS, to secure transmitted data. It also offers user authentication mechanisms using LDAP, Active Directory, and OAuth for proper secure access control.
Automation:
The automation capability of CrushFTP allows automating the everyday routine tasks through schedule-based transfer, event-based triggers, and custom flow. This ensures that the batch processing is effective with minimum manual interruption, improving productivity.
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Remote Administration:
CrushFTP supports remote administration through the web interface. This allows an administrator to manage server settings, user permissions, and file operations from any part of the world that is connected to the Internet. In this regard, it gives a very nice distributed team and remote work environment.
Integration:
The software easily integrates with third-party applications and services through a very extensive API, as well as through support for plenty of plugins. This way, it becomes straightforward for organizations to fit CrushFTP into their already existing infrastructure to promote interoperability and ensure scalability.
Monitoring and Logging:
CrushFTP provides very detailed tracking and logging where an administrator can trace all user activities, monitor the performance of the server, and analyze network traffic. It also offers real-time alerts and notifications for proactive management and troubleshooting.
Customization:
Make CrushFTP work with any possible parameters in mind through configurable settings, themes, and extensions
AI is revolutionizing DevOps by advancing algorithmic optimizations in pipelines, elevating efficiency levels, and introducing predictive functionalities. This article examines how AI is reshaping continuous integration, deployment strategies, monitoring practices, and incident management within DevOps ecosystems, ultimately amplifying efficiency and dependability.
Unlocking the Future of Artificial IntelligencedorinIonescu
Unlock the Future: Dive into AI Today! Videnda AI specializes in developing advanced artificial intelligence solutions, including visual dictionaries and language learning tools that leverage immersive virtual travel experiences. Stay Ahead of the Curve: Master AI Now! Our AI technology integrates machine learning and neural networks to enhance education and business applications. AI: The Next Frontier. Are You Ready to Explore? With a focus on real-time AI solutions and deep learning models, Videnda AI provides innovative tools for multilingual communication and immersive learning.
In this course, you'll find a series of engaging videos packed with vibrant animations that break down complex AI concepts into digestible pieces. Our curriculum covers AI models such as Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Generative Adversarial Networks (GAN), and Transformers, providing a solid understanding of these models and their real-world applications. We also offer hands-on experience with Generative AI tools like ChatGPT and Midjourney, and Python programming tutorials to help you implement AI algorithms and build your own AI applications.
We are proud participants in the Nvidia Inception Program, driving AI innovation across various industries. By the end of our course, you'll have a strong understanding of AI principles, enhanced Python programming skills, and practical experience with state-of-the-art Generative AI tools. Whether you're looking to kickstart a career in AI or simply curious about this revolutionary technology, Videnda AI is your partner in mastering the future of artificial intelligence.
How to Secure Your Kubernetes Software Supply Chain at ScaleAnchore
Achieving comprehensive security visibility in Kubernetes environments is essential for maintaining robust and compliant cloud-native applications. In this exclusive webinar, Anchore and Spectro Cloud team up to showcase how to enhance your Kubernetes security posture with SBOM (Software Bill of Materials) management and vulnerability scanning.
Join Cornelia Davis, VP of Product, Spectro Cloud and Alan Pope, Director of Developer Relations, Anchore to learn how to elevate your Kubernetes security visibility and protect your cloud-native applications effectively.
—Discover how Anchore can be integrated with Spectro Cloud Palette to take SBOM scanning to the next level, delivering fully automated software compliance
—Gain valuable insights into best practices for securing your Kubernetes workloads, ensuring compliance, and improving your DevSecOps processes.
Fix Production Bugs Quickly - The Power of Structured Logging in Ruby on Rail...John Gallagher
Rails apps can be a black box. Have you ever tried to fix a bug where you just can’t understand what’s going on? This talk will give you practical steps to improve the observability of your Rails app, taking the time to understand and fix defects from hours or days to minutes. Rails 8 will bring an exciting new feature: built-in structured logging. This talk will delve into the transformative impact of structured logging on fixing bugs and saving engineers time. Structured logging, as a cornerstone of observability, offers a powerful way to handle logs compared to traditional text-based logs. This session will guide you through the nuances of structured logging in Rails, demonstrating how it can be used to gain better insights into your application’s behavior. This talk will be a practical, technical deep dive into how to make structured logging work with an existing Rails app.
I talk about the Steps to Observable Software - a practical five step process for improving the observability of your Rails app.
Literals - A Machine Independent Feature21h16charis
Introduction to Literals, A machine independent feature. The presentation is based on the prescribed textbook for System Software and Compiler Design, Computer Science and Engineering - System Software by Leland. L. Beck,
D Manjula.
iBirds Services - Comprehensive Salesforce CRM and Software Development Solut...vijayatibirds
Unlock the full potential of your business with iBirds Services. As a trusted Salesforce Consulting Partner, iBirds Software Pvt. Ltd. offers a wide range of customer-centric consulting services to help you seamlessly integrate, customize, and optimize your Salesforce CRM. Our team of experts specializes in delivering innovative software development solutions tailored to meet your unique business needs.
In this document, you will discover:
An overview of iBirds Services and our expertise in Salesforce CRM implementation.
Detailed insights into our software development services, including custom applications, integrations, and automation.
Case studies highlighting our successful projects and satisfied clients.
Key benefits of partnering with iBirds Services for your CRM and software development needs.
Whether you are a small business or a large enterprise, our proven strategies and cutting-edge technologies ensure your business stays ahead of the competition. Explore our services and learn how iBirds can transform your business operations with scalable and efficient solutions.
Monitoring the Execution of 14K Tests: Methods Tend to Have One Path that Is ...Andre Hora
The literature has provided evidence that developers are likely to test some behaviors of the program and avoid other ones. Despite this observation, we still lack empirical evidence from real-world systems. In this paper, we propose to automatically identify the tested paths of a method as a way to detect the method’s behaviors. Then, we provide an empirical study to assess the tested paths quantitatively. We monitor the execution of 14,177 tests from 25 real-world Python systems and assess 11,425 tested paths from 2,357 methods. Overall, our empirical study shows that one tested path is prevalent and receives most of the calls, while others are significantly less executed. We find that the most frequently executed tested path of a method has 4x more calls than the second one. Based on these findings, we discuss practical implications for practitioners and researchers and future research directions.
In today's dynamic business landscape, ERP software systems are essential tools for businesses worldwide, including those in the UAE. These systems cater to the unique needs of the UAE's rapidly changing economy and expanding industries.
This blog examines the top 10 ERP companies in the UAE, highlighting their innovative products, exceptional customer support, and significant impact on the regional business community. These companies excel in providing ERP solutions that enhance efficiency and growth for businesses throughout the UAE.
1. **Odoo**
- Odoo ERP is a comprehensive business management solution with features like accounting, HR, sales, inventory control, and CRM. Its user-friendly interface simplifies processes and boosts productivity. Banibro IT Solutions leverages Odoo to transform business operations.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Open Source: Yes
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Chat, Email
- Payment: Yearly, Monthly
- Multi-Language: Yes
- OS Support: Windows, Mac, iOS, Android
- API: Available
2. **Microsoft Dynamics 365**
- Dynamics 365 offers a centralized platform for small and medium-sized businesses, integrating with Microsoft apps and cloud services for scalability. It simplifies data processing with user-friendly interfaces and customizable reporting.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, iOS, Android
- API: Not specified
3. **FirstBIT ERP**
- Known for serving small and medium-sized businesses, FirstBIT ERP offers comprehensive solutions and exceptional customer service, enhancing productivity and efficiency.
- **Details:**
- Suitable for: Medium, Large Businesses
- Open Source: Yes/No
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Email, Video Tutorials
- Payment: Yearly, Monthly
- Multi-Language: Yes
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Available
4. **Ezware Technologies**
- Ezware Technologies provides top-notch ERP solutions for various industries with user-friendly modules that streamline complex business processes.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Not specified
5. **RealSoft**
- RealSoft by Coral is popular in Dubai, offering modules for contracting, real estate, job costing, manufacturing, trading, and finance. It's VAT-enabled and affordable for medium-sized businesses.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Open Source: No
- Cloud-based: On-premises
-
The code is written and the tests pass. I just have to commit this last round of changes to my branch. Wait, why does that say committed to main? Did I commit all those changes to main? Arghh! I can’t redo all of this!
Committing changes to the wrong branch, forgetting files, misspelling the commit message, and needing to undo commits are some of the “advanced” features of Git that we normal people run into way too often and need help with. The fixes are often easy – once you know what they are. But in the heat of the moment, with the deadline (or Friday afternoon) approaching, it isn’t always easy to figure out what magic spell to cast to get Git to do what you need.
We’ll spend some time looking at typical Git situations people get themselves into, and then we’ll demonstrate how to get out of them. This isn’t about Git internals or a Git master’s class – this real-world Git when things aren’t going right. And there will be plenty of time for questions, so bring your “best” Git nightmare scenarios so we can figure out how to recover.