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Blockchain Technology

Creative jobs website and networking platform The Dots has launched the “bias blocker”, a new browsing mode that stops employers being able to see candidates’ photos, names, education, and employment history, on the basis that they should be judged on the “quality of their work” only. Will tools like this help to build a more diverse design workforce?

Regardless of our gender, race, religion, cultural beliefs or education, all of us are biased in some way. This no doubt seeps into recruitment – whether we are subconsciously looking for people we relate to, or we are actively going against this to try to diversify the workplace, personal feelings and opinion are making their way into the hiring process.

Positive discrimination – giving an advantage to those from minority backgrounds or discriminated groups to put them on a level playing field with others – can only be a good thing. It increases the diversity of people, and therefore the diversity of ideas, in the workplace.

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EMR Clusters & Usage in Data Analytics

Amazon EMR is a managed cluster platform that helps us in running big data frameworks, such as Apache Hadoop, on top of Amazon EC2 instances to process and analyze vast amounts of data.

EMR (Elastic MapReduce) is a cloud-based big data platform that enables you to process large datasets using open-source tools such as Hadoop, Spark, and Hive. It simplifies the process of setting up, managing, and scaling Hadoop clusters, making it easier to process and analyze large volumes of data. In this blog, we will explore the key features of EMR clusters and how they are used in data analytics.

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Hadoop Distributed File Systems

Architecture of HDFS:

The HDFS architecture consists of two main components: NameNode and DataNode. The NameNode manages the metadata of the file system, including the directory structure, file names, and permissions. The DataNode stores the actual data.

HDFS works by dividing large files into smaller blocks, typically 128MB or 256MB in size. These blocks are then distributed across multiple machines in the Hadoop cluster. Each block is replicated across several machines to ensure fault tolerance.

The NameNode keeps track of the location of each block and the replication factor. It also manages the allocation of new blocks and the deletion of old blocks.

Data processing with HDFS:

HDFS is designed to work seamlessly with other Hadoop components, such as MapReduce and YARN. MapReduce is a programming model used for processing large datasets, while YARN is a resource management system that allocates resources to running applications.

When processing data with Hadoop, the MapReduce framework reads data from HDFS and processes it in parallel across multiple machines. The results are then written back to HDFS.

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Apache Airflow and its Architecture

Apache Airflow and how to schedule, automate, and monitor complex data pipelines by using it. we discuss some of the essential concepts in Airflow such as DAGs and Tasks.

Understanding Data Pipelines: As you can see in the following diagram the phases we discussed in the earlier segment — Extraction > Storing raw data > Validating > Transforming > Visualising — can also be seen in the Uber example.

Apache Airflow is an open-source platform used for scheduling, automating, and monitoring complex data pipelines. With its powerful DAGs (Directed Acyclic Graphs) and task orchestration features, Airflow has become a popular tool among data engineers and data scientists for managing and executing ETL (Extract, Transform, Load) workflows.

In this blog, we will explore the fundamental concepts of Airflow and how it can be used to schedule, automate, and monitor data pipelines.

DAGs and Tasks

The fundamental building blocks of Airflow are DAGs and tasks. DAGs are directed acyclic graphs that define the dependencies between tasks, while tasks represent the individual units of work that make up the pipeline.

In Airflow, DAGs are defined using Python code, and tasks are defined as instances of Operator classes. Each task has a unique ID, and operators can be chained together to create a workflow.

For example, suppose you have a data pipeline that involves extracting data from a database, transforming it, and then loading it into another database. You could define this pipeline using a DAG with three tasks:

The Extract task, which retrieves data from the source database

The Transform task, which processes the data

The Load task, which writes the processed data to the target database

Each task is defined using a specific operator, such as the SQL operator for extracting data from a database, or the Python operator for running Python scripts.

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ChatGPT-3.5 vs ChatGPT-4

As a language model developed by OpenAI, ChatGPT has become increasingly popular among developers and businesses for its ability to generate human-like responses to text prompts. The release of ChatGPT-4, the latest version of the model, has generated a lot of interest in the AI community, with many wondering what makes this version better than its predecessor, ChatGPT-3.5

Conclusively, ChatGPT-4 represents a significant improvement over its predecessor, ChatGPT-3.5. With its increased model capacity, improved understanding of context, enhanced efficiency, and multilingual support, ChatGPT-4 is poised to become an even more valuable tool for businesses and developers looking to integrate AI into their applications and services.

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Amazon Redshift and how it is important in Big Data Analytics

Amazon Redshift is a cloud-based data warehousing service that provides a fast, reliable, and cost-effective way to analyze large volumes of big data. It is designed for data warehousing and analytics applications and is optimized for querying and processing large datasets.

Amazon Redshift is a powerful data warehousing service that provides a fast, reliable, and cost-effective way to analyze large volumes of data. Its clustered architecture, columnar storage, and support for SQL extensions make it ideal for analytics and data warehousing applications. With its scalability and integration with other AWS services, Amazon Redshift is a key component of many big data architectures.

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Top Mobile App Design Trends to Lead the Market in 2024

As we move towards 2024, mobile app design trends continue to evolve with the latest technological advancements. User experience, simplicity, and functionality are the key drivers that define these design trends.

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Best Cross-Platform Mobile Development Tools

In today's world, mobile applications have become an integral part of our lives. With the increasing number of mobile devices, developers need to develop mobile apps that can run on multiple platforms. Cross-platform mobile development tools have made this possible, allowing developers to create mobile applications that can run on multiple platforms with a single code base.