Apache Ozone is an object storage solution designed for big data applications. Big data workloads are significantly different from standard workloads, and Ozone is born out of experiences gained from running Hadoop on thousands of clusters. Managing and analyzing massive datasets has become central to technology today. In this context, Ozone, with its features in data storage and management, offers a powerful and scalable solution for large-scale data projects, reshaping the future of data engineering.

Understanding the Needs of Big Data Environments

When selecting a storage system for a big data ecosystem, the following criteria are paramount:

Performance: The system must efficiently handle high-volume data loads.

Scalability: It should be capable of expanding to hundreds of petabytes of data, thousands of nodes, and billions of objects.

API Compatibility: Seamless operation with APIs like S3 and modern architectures (cloud, container, etc.) is essential.

Let’s delve into how Apache Ozone meets these expectations.

Ozone: Object Storage Optimized for Big Data Workloads

Apache Ozone is a highly scalable, redundant, and distributed object storage technology optimized for big data workloads.

Key features include:

S3 and FS Interfaces: Supports both S3 API and file system-like interfaces.

Designed for Big Data Workloads: Optimized for big data analytics and processing.

Billions of Objects: Capable of storing a massive number of files (objects).

YARN or Kubernetes Compatibility: Can be easily used in container-based environments.

Scales to Thousands of Nodes: Expand your system horizontally to store much more data.

Low Cost: Offers the ability to store more data with less hardware compared to HDFS.

Figure 1: Ozone Architecture

Fundamental Storage Elements of Ozone: Volume, Bucket, and Key

Volume: Similar to user accounts or tenant concepts. Each volume can contain one or more buckets.

Bucket: Functions similarly to a bucket in the Amazon S3 world. A bucket can hold an unlimited number of keys.

Key: Similar to files. Each key belongs to a specific bucket, and each bucket belongs to a specific volume.

Two Management Components: Ozone Manager (OM) and Storage Container Manager (SCM)

The Ozone architecture features two critical components responsible for management tasks:

Ozone Manager (OM): Manages namespaces. When you want to write a key (file), OM gives you a block and remembers which volume/bucket this block is in. In other words, OM manages the metadata of volumes, buckets, and keys.

Storage Container Manager (SCM): Creates and manages containers, which Ozone uses as replication units. While HDFS replicates at the block level, Ozone replicates at the container level, offering significant advantages, especially as scale increases.

DataNodes

These are the nodes where all the data is stored. The client sends data in blocks, and these blocks are stored inside a storage container by the datanodes.

Recon Server

Recon is a monitoring service that tracks metadata stored by different components, such as OM and SCM, in the Ozone cluster, providing information about this data.

Ozone is a multi-protocol storage that supports the following interfaces:

– s3: Amazon’s Storage Service (S3) protocol. With the S3 Gateway, you can use S3 clients and S3 SDK-based applications on Ozone without making any changes to Ozone.

– o3: An object storage interface accessible from the Ozone shell.

– ofs: A Hadoop-compatible file system that allows any application expecting an HDFS-compatible interface to run on Ozone without API changes.

Advantages of Ozone

Better Performance:

Queries run faster, data processing speeds up.

4x Storage Capacity:

Scalability up to 384 TB per node allows you to manage massive amounts of data on data-intensive nodes simultaneously.

10x Scalability:

Ability to store up to 10 billion objects, exceeding HDFS’s 400 million file limit, provides a critical advantage for large-scale data projects.

Easier Management:

Linear scalability, fast recovery, and low maintenance costs offer a more comfortable experience for administrators.

Lower Cost per TB:

More efficient use of hardware resources, requiring less physical storage for the same data volume.

Ozone vs HDFS

Why Cloudera Chose Ozone?

Cloudera continuously evaluates solutions supporting object storage in the big data world. Apache Ozone stands out as a powerful and scalable object storage system designed to replace HDFS, while also being suitable for large-scale data projects. Additionally, HDFS and Ozone can coexist within the same cluster in Cloudera.

Key reasons for Cloudera choosing Ozone:

Compatibility with Hadoop Ecosystem:

Ozone seamlessly integrates with the existing data infrastructure as it is designed as a natural part of the Hadoop ecosystem. It simplifies data analytics and processing processes while providing a modern object storage layer.

High Performance and Scalability:

It offers significant performance improvements in data writing and reading operations in big data workloads and provides the ability to scale to billions of objects.

Distributed and Highly Available:

By storing data in a distributed structure, the risk of data loss is minimized even if a node fails, and access continues uninterrupted.

Figure 2: Ozone-HDFS Performance

Benchmark Results: Ozone Outperformed HDFS

Cloudera compared HDFS and Ozone using a widely adopted benchmark test like TPC-DS. They ran a total of 99 queries on two separate clusters, each consisting of 12 storage and compute nodes with 100 GB and 1 TB datasets, respectively, alongside key Hadoop ecosystem components like Hive, Tez, and YARN.

The results showed that Ozone outperformed HDFS by an average of 3.5% in terms of query completion time for both dataset sizes, demonstrating a performance advantage.
For a more detailed analysis, refer to: Benchmarking Ozone – Cloudera Blog 

Conclusion

In summary, the high performance, exceptional scalability, and multi-protocol support offered by Apache Ozone make it a powerful and future-proof solution for big data projects. Cloudera’s adoption of Ozone and the benchmark tests conducted clearly demonstrate that this technology will rapidly spread across the industry.

If you are looking for a platform that will take your big data infrastructure to the future and simplify your data storage and management processes, Apache Ozone should definitely be on your radar.

Prepared by: GTech Database and Big Data Platform Team

The post Powerful Solution for Big Data Projects: Apache Ozone appeared first on GTech.

Microsoft Fabric is Microsoft’s data integration and analytics platform. It brings together data engineering, data management, data analysis, and business intelligence functions. Microsoft Fabric aims to make data operations more efficient and scalable by providing all these functions on a single integrated platform. In today’s data-driven world, businesses have recognized the need to manage data on secured platforms that provide a single source. In this context, Microsoft introduced Fabric in November 2023, a product that encompasses everything from data transport to data science, real-time analysis, and business intelligence. This platform, aiming for simplicity, consolidates its services on Azure under a Software as a Service (SaaS) model. Consequently, data engineers, analysts, or scientists can now collaborate more effectively.

Microsoft Fabric Features

Cloud-Based Infrastructure

Cloud computing is the delivery of computing services (including servers, storage, databases, networking, software, analytics, and artificial intelligence) over the Internet (‘the cloud’) to provide faster innovation, flexible resources, and economies of scale. Cloud infrastructure consists of the hardware and software components used to deliver cloud computing services.

Data Management and Integration

Data management encompasses the processes, principles, roles, measurements, and standards that enable the effective and efficient use of information. Data integration is the process of combining data from different sources to provide users with a single, unified view.

High Performance and Scalability

Performance refers to the efficiency of a system under a specific workload over a given time. Scalability involves parameters that we can adjust to enhance performance when needed. In the cloud, we validate performance by testing scalability. Examples of items you should measure include:

Resource Utilization:

– CPU Load
– Memory Usage
– Disk I/O
– Read/Write Database Queries

Application Statistics:

– Number of Requests
– Response Time

Security and Compliance

To identify and protect against rapidly evolving threats, it is essential to implement multi-layered built-in security measures, avoid misconfigurations, and establish safeguards in your resources to ensure consistent resource management.

AI and Machine Learning Support

The Synapse Data Science service in Microsoft Fabric provides a Software as a Service (SaaS) experience that assists machine learning experts in building, deploying, and preparing machine learning and artificial intelligence models on a single analytics platform, while also collaborating with other key roles.

One of the key features Microsoft implements with Fabric is Microsoft Copilot for data insights. This AI-powered assistant can answer questions related to data. Since it is connected to the broader Fabric ecosystem, you can apply it to data across your organization.

Comprehensive Analytics and Reporting Tools

Data scientists have access to powerful tools and services for complex data analysis and processing, such as Apache Spark integration. Microsoft’s flagship business analytics tool, Power BI, is set to leverage numerous advantages from Fabric, including:

Enhanced Data Access: Fabric ensures seamless data ingestion by allowing Power BI to pull data from a wider range of sources. Direct connections from Power BI to Delta tables in the data lake eliminate the need for maintaining a SQL layer, resulting in significant cost savings.

Real-Time Analysis: Streamlined data processing enables lower-latency analyses, facilitating quicker decision-making.

Unified Reporting: As Fabric breaks down data warehouses, reports can pull data from multiple Azure services, leading to more comprehensive reporting.

User Experience and Interface Features

Power BI and other integrated tools provide intuitive interfaces that allow non-technical users to explore data and create basic reports.

Microsoft Fabric Components

Microsoft Fabric offers a comprehensive suite of analytical experiences designed to work seamlessly together. The platform tailors each of these experiences to specific personas and tasks.

Fabric hosts three main data analysis products by Microsoft: Power BI, Azure Data Factory, and Azure Synapse (Figure 1).

Figure 1. Microsoft Fabric Components

Data Engineering:

Microsoft Fabric provides data engineers with a specialized infrastructure and system set designed to handle large volumes of data, enabling users to effectively manage them.

Data Science:

Microsoft Fabric offers a comprehensive data science experience that allows users to successfully execute end-to-end data science workflows for data enrichment and gaining business insights. Users can successfully complete a wide range of activities throughout the data science process, from data discovery, preparation, and cleansing to experimentation, modeling, model scoring, and prediction. This process extends to integrating the insights gained into BI reports.

Data Warehouse:

Users can create virtual data warehouses to query desired data across databases. These warehouses can source data from OneLake, Azure Data Lake Storage, or other cloud vendor storages without duplicated data within a single boundary. Queries can be performed using SQL technology combined with Apache Spark for handling big data. Alternatively, users can utilize the Visual Query editor, which provides a code-free alternative to SQL queries (Figure 2). Additionally, these systems can scale up as usage demands increase or scale down when not needed, without requiring user intervention.

Figure 2. SQL Queries

Data Factory:

Data Factory combines the best features of Azure Data Factory and Microsoft Power Query. Known as the Azure version of SSIS (SQL Server Integration Services), Data Factory enables data movement and transformation, allowing for complex data integrations and ETL scenarios. Here, activities such as data transformation using dataflow and data copying via data pipeline can be managed, supporting one or multiple activities.

Figure 3. Data Pipeline

Real-Time Analytics:

Applications we use, IoT devices, and high-level operational systems result in large-scale data. Systems that move this data’s ETL to data warehouses and run ETL processes at specified times may not suffice. Therefore, data flow systems are created to collect changes occurring in source systems through mechanisms like event processing and APIs. Microsoft Fabric features real-time analytics for data flow. On this platform, data is gathered from multiple sources using Eventstream and directed to relevant destinations (Lakehouse, KQL Database, Custom App). Queries can be prepared, saved, and shared using the KQL Query Set. Lastly, real-time reports and dashboards can be created using Power BI.

Advantages of Microsoft Fabric

Microsoft Fabric is a versatile platform that offers a wide range of benefits for businesses and individuals. With a focus on seamless integration and scalable solutions, Microsoft Fabric has become an indispensable tool for those looking to enhance productivity, streamline operations, and improve overall digital experiences.

Flexibility and Customization Options

The flexibility of Microsoft Fabric is a significant advantage. It caters to a wide variety of workloads and applications, making it suitable for various industries and purposes. Whether you need to host a website, run a data analytics project, or deploy a mobile application, Microsoft Fabric provides the tools and resources to meet your specific needs. This adaptability is vital in a fast-paced, ever-changing digital environment.

Enhanced Collaboration Capabilities

Microsoft Fabric includes features that support collaboration and communication within your organization. With tools like Microsoft Teams and SharePoint, your teams can collaborate more effectively regardless of their physical locations. Real-time document sharing and video conferencing capabilities enhance productivity and create a collaborative working environment.

Cost Optimization and Return on Investment

Microsoft Fabric helps you optimize your spending by eliminating the need for a significant upfront investment in hardware and software, significantly reducing your capital expenditures. Moreover, the pay-as-you-go pricing model allows you to pay only for the resources you use, resulting in substantial long-term savings. This makes Microsoft Fabric a cost-effective solution for organizations of all sizes.

Comprehensive Data Security

Microsoft Fabric provides robust security measures to protect your data and applications. It includes features aimed at safeguarding sensitive information, such as identity and access management, data encryption, threat detection, and compliance management. With Microsoft Fabric, you can have peace of mind knowing your digital assets are well-protected.

Rapid and Easy Deployment

Whether it’s Microsoft Azure, Office 365, or any other Microsoft tool, Fabric ensures that your digital ecosystem functions as a cohesive unit, saving time and reducing operational friction.

Integration and Compatibility

One of the standout features of Microsoft Fabric is its ability to integrate seamlessly with other Microsoft products and services. This integration simplifies workflows by allowing data and applications to work together effortlessly. Whether it’s Microsoft Azure, Office 365, or any other Microsoft tool, Fabric ensures that your digital ecosystem operates as a unified unit, saving time and minimizing operational friction.

Industry Applications of Microsoft Fabric

Microsoft Fabric in Healthcare

The healthcare data solutions in Microsoft Fabric provide an end-to-end analytics SaaS platform that allows you to ingest, store, and analyze health data from various sources, such as electronic health records, imaging archiving, and communication systems. These solutions offer data models and transformation activities that help customers build multimodal repositories. With the healthcare data solutions in Microsoft Fabric, you can access and query your health data using familiar experiences like Azure Synapse Analytics, Azure Data Factory, and Microsoft Power BI.

 Microsoft Fabric in Finance and Banking

Microsoft Fabric is not a finance-focused product. Instead, it is a platform that enables individuals across various departments, including finance and accounting, to benefit from robust data and analytics capabilities.

Frequently Asked Questions about Microsoft Fabric

What products can I create user groups for?

User groups can be created for Power BI, Synapse, and Data Factory.

What is the migration path for existing customers using Data Factory and Synapse?

Existing customers using Data Factory and Synapse will continue to receive support, and there is no urgent need for migration. The Data Factory team is developing migration tools for customers wishing to transition to Fabric, ensuring a smooth transition. While the team is committed to ongoing investment in Data Factory and Synapse, future feature updates may focus on Fabric.

What is the difference between the data factory in Fabric and the data engineering tab?

Data Factory helps you solve complex data integration and ETL scenarios with cloud-scale data movement and transformation services, while data engineering helps you build a data lake and use Apache Spark to transform and prepare your data. Differences between the terminologies/experiences in Fabric are available under Microsoft Fabric terminology.

What is the difference between Fabric and Synapse?

The main difference lies in data storage. Unlike Azure Synapse Analytics, Microsoft Fabric does not have a dedicated SQL pool or relational storage. Instead, the repository data is persisted in delta lake format within OneLake. Additionally, since Fabric is a SaaS, you do not need to create and manage Apache Spark pools.

What are the differences between Microsoft Fabric and other Microsoft solutions?

In recent years, Microsoft has launched a series of products and features that fundamentally change the data and analytics landscape. The latest iteration in this series is Microsoft Fabric. Microsoft describes Fabric as a “unified analytics platform.” So, what does this really mean?

In many ways, Microsoft Fabric can be seen as a rebranding and repositioning of its existing services, but with added features and functionalities. This represents a new and unified approach to how we process our data. To better understand Fabric’s role within the ecosystem, Microsoft depicts the following diagram as a new and refreshed umbrella encompassing many tools you may already be using.

What are the main advantages of Microsoft Fabric?

– Reduces the need for data copying for different OneLake solutions.
– Allows company users to focus on complex objectives, such as machine learning or data science projects.
– Reduces the complexity of pricing models by introducing universal computing capabilities.
– Includes data analysis platforms like Azure Data Factory, Data Explorer, and Apache Spark, integrated as an additional package within Microsoft Fabric.
– Utilizes open-source document formats and Delta Lake.
– Simplifies model architecture and enables immediate, actionable insights.

What technical skills do I need to use Microsoft Fabric?

If you are familiar with tools like Power BI, you won’t need to adapt most of your approaches to use Microsoft Fabric. Remember, this is a unified platform aimed at breaking down walls between business users. Most companies do not deploy Microsoft Fabric directly; it is typically managed by IT teams with existing Microsoft setups.

Is Microsoft Fabric cloud-based, or can it be used on-premises as well?

Microsoft Fabric is used as SaaS on Azure, so it cannot be used on-premises.

How is Microsoft Fabric set up, and what are the first steps?

The entire interface/access of Microsoft Fabric operates through the Power BI service. So, it’s a familiar environment! A user with the Power BI Admin, Power Platform Admin, or Global Admin role can activate Fabric for your entire tenant or for a specific user group. If you have a Power BI Premium license, you can also activate Fabric for the capacity you purchased.

You can activate Fabric from the tenant settings on the Power BI Admin page.

Microsoft 365 Developer Account: This license provides you access to a Power BI Pro account.

Log in to Microsoft Power BI with your Developer Account.

Activate your Microsoft Fabric tenant.

Which software can be integrated with Microsoft Fabric?

The main components that can be integrated with Microsoft Fabric include:

– Power BI
– Data Factory
– Data Activator
– Industry Solutions: Manufacturing data solutions in Microsoft Fabric (preview) and copilot template for factory operations on Azure AI, retail data solutions in Microsoft Fabric, sustainability data solutions in Microsoft Fabric.
– Real-Time Intelligence
– Synapse Data Science
– Synapse Data Warehouse

What are the costs associated with Microsoft Fabric?

Power BI Pro or PPU licenses are not sufficient for Fabric. If you hold a Premium license, you can add Fabric features to the capacity you have purchased.

Sources: 

Source: Erwin de Kreuk Blog – Real-Time Analytics in Microsoft Fabric

Source: Atlan – What is Microsoft Fabric

Source: İlhami Demirci (Medium) – Microsoft Fabric 101

Source: Microsoft Learn – Microsoft Fabric Overview

Source: Enterprise DNA Blog – What is Microsoft Fabric

 
Prepared by: GTech DWH & BI Team

The post Microsoft Fabric: The Innovative Solution That Enhances Your Power in Digital Transformation appeared first on GTech.

How was ElasticSearch born?

ElasticSearch is a full text search engine and analysis tool developed on the Apache Lucene infrastructure using Java programming language. Lucene, developed to perform searches on huge text files on a single machine, was insufficient for searches on instant data and distributed systems; so ElasticSearch was born and it has gained popularity in a short time with its flexible structure and capabilities such as working with real-time data in distributed systems.

What are the Advantages of ElasticSearch?

• It can operate in a distributed and highly scalable structure.
• It is open sourced.
• It has RESTful API support.
• It is close to the reality. This way, datas can be found via searches with ElasticSearch seconds after they have been saved.
• It can be backed up easily.
• It works using very few resources compared to its alternatives.
• It has a simple cluster structure.
• Since it indexes, it returns search results quickly.
• It indexes documents as JSON, it supports many programming languages.
• It can be mapping according to the data type.
• It has fast and easy installation.
• It allows data transfer from databases such as MongoDB, NoSQL, Cassandra and HBase to ElasticSearch.
• It has an auto-complete feature.

How does ElasticSearch Works?

While saving any data to ElasticSearch, previously determined fields in the data are indexed. Since ElasticSearch performs this process at the first moment of the data record and classifies the data according to the index list, it can reach the search results quickly.

What are ElasticSearch’s Basic Concepts?

• Indice: While searches are performed through databases in classical search engines, ElasticSearch searches are performed over index files called “Indice”. There could be more than one database (Indice) in ElasticSearch Cluster.
• Document: It represents the rows in Type structures in ElasticSearch and Type’s are made up of these structures.
• Mapping: It is the process of defining the data type of the indexed data.
• Field: Column in other database types is called Field in ElasticSearch. Each Document can contain more than one Field.
• Cluster: It is the sum of one or more nodes. Thanks to the cluster, index creation and search capabilities that include all data are created.

GTech Big Data and Advanced Analytics services providing consultancy services to determine the most suitable big data platform and capacity for the big data needs of the enterprises. It also undergoes a health screening of the existing big data ecosystems and makes the most appropriate development recommendations.

You can find more detailed information about the products and services our web site.

Prepared by: GTech Database and Big Data Platform Team

The post What Is ElasticSearch? appeared first on GTech.

What are the Features of MongoDB?

• It has a scalable structure.

• Supports range queries, field searches and regular definition searches.
• It can index any field in the document in Binary BsON format.
• It can create multiple copies of the original data and thus prevent data loss.
• It allows data to be processed collectively as well as collecting data.
• Driver support is available for most of the current programming languages

Companies Using MongoDB

As we mentioned earlier, MongoDB is a popular database application around the world. Today, MongoDB, which is used by many businesses, is also preferred by some companies with worldwide recognition.

How to Use MongoDB?

MongoDB contains many commands that can be used for different purposes. In this article, we will show you how to run a few of the most used commands with MongoDB.

• Mongo: It is the command required to run the MongoDB database application. After opening the MongoDB command screen, type the “mongo” command to run MongoDB.

• Show databases: The “show databases” command is used to list the databases in the system. When this command is entered, the databases in the system and the list of the dimensions of these databases are displayed.

• Use: This command is used to create a new database or to open an existing database. For example; If the command “use NewDataBase” is entered and there is no database named NewDataBase in the system, this database is created. After the database is created, the message “switched to db NewDataBase” appears on the screen. If we use the “use” command with an existing database name, it will switch to that database.

• dropDatabase(): This command is used to delete an existing database. The usage method is “use DatabaseName” and after selecting the database, “db.dropDatabase()” command is given and the relevant database is deleted from the system.

• createCollection (“newtable”): When this command is used, a new table is created in the database. As in the previous operations, first run the command “use DatabaseName” and then execute the db.createCollection(“tablename”) command to create a new table.

• Show collections: This command is used to list the tables in the database.

• tablename.drop(): We use this command when we want to delete any of the tables in the database.

• tablename.insert({}): Using this command, we can add data to a table in the database. For example; When we enter a command db.tabloname.insert({“category”: “shoes”, “color”: “black”, “number”:”42″}), we add black shoes with size 42 to the table.

For more detailed information about GTech’s products and services, please contact us.

Prepared by: GTech Database and Big Data Platform Team

The post What Is MongoDB? appeared first on GTech.

Being a complete enterprise level platform, Striim offers an end-to-end solution with detailed and comprehensive data flow analysis, data visualization functions and creates automatic notifications to accurately identify time sensitive and critically important information real-time data integration.

Advantages of Striim

• It offers real-time data feeding, streaming data processing and monitoring, real-time data distribution in a single application.
• By performing transformation, enrichment, filtering and estimation processes on data flowing in cloud (Cloud) or on-premise (On-Premise) environments, it provides correlation with reference data and provides easy migration of the data center to the cloud thanks to simplified real-time data synchronization.
• It performs real-time data transfer to Microsoft Azure, Amazon Web Services (AWS) and Google Cloud environments.
• It continuously reads data from data sources with high speed and volume, such as log files, message systems, Hadoop and IoT data, through CDC, which requires a low resource. In this way, it continuously and instantaneously carries the data changes that occur as new transactions occur in the database, it ensures that up-to-date information is analyzed and fast solutions are found for time sensitive problems.
• It performs different notifications such as real-time e-mail, short message (SMS), web alert, push notification by adhering to the rules defined on the flowing data.
• It provides real-time integration of data without causing any slowdown or change in source systems.
• It enriches the data in motion by preprocessing it with reference data in real time and enables it to be analyzed accurately.
• It continuously verifies the data consistency between source and destination databases.
• By minimizing network usage, it extends the service life of the servers and reduces the cost of ownership.
• By establishing a relationship between more than one data source, it provides meaning to situations that cannot be detected from a single data source.
• Provides Performance in Sub-Seconds: The processing of data produced within milliseconds continues without any delay.
• It has a minimal impact since it does not interfere with Log-Based CDC and relational databases.
• With its real-time monitoring and verification feature, it allows to monitor the correct and healthy data processing and flow.
• Striim’s high-speed message infrastructure uses the Data Grid (data network), IMC for event stream processing and visualization.
• It allows time, event and session based windowing.
• It provides real-time data integration between On-Premise and Cloud systems.

To have an end-to-end Big Data solution with Striim, to perform real-time data analysis, to generate value with the new generation in-memory solution and to get the maximum benefit from your existing infrastructure, contact us!

Prepared by: GTech Database and Big Data Platform Team

The post Striim – Real Time Data Integration appeared first on GTech.

How Is Big Data Used?

Big data contributes significantly to firms’ ability to make right decisions and develop strategies by examining customer behavior. After the available data are turned into simplest and most processable data, the relationship of such data with each other is examined using the comparison method and the connections between them are revealed. In this way, it is possible to predict the results of the decisions to be taken. With the simulations created by changing the location of various points in the data, reactions to different decisions can be seen.

Thanks to Big data analytics, organizations can accurately evaluate data based on real customer behavior and turn it into a high-benefit tool.

The Importance of Big Data

Since big data is based entirely on the analysis of real data, it allows making the right decisions in many different areas such as reducing costs, spending advertising on the right channels, saving workforce and developing products that meet expectations.

Uses of Big Data

Big data can be used in all areas, especially in areas such as banking and retail industry, where customer behavior is very important and needs to be followed. In recent years, the importance of big data has been understood by governments and started to be used in various fields. In the field of health; big data is used for early diagnosis of diseases or drug development can also be used to prevent crimes or to make improvements in the education system.

What Are Big Data Components?

Big data consists of 5 main components.

• Variety: Most of the data produced have different formats from each other. Data from phones, tablets, computers; different operating systems or languages cause different formats to emerge.
• Velocity: Technological possibilities increasing day by day cause the amount of data obtained, the number of transactions to be made and diversity to increase in the same way.
• Volume: According to IDC’s “Digital Universe” study, the world’s data volume was projected to grow 44-fold between 2009 and 2020, while IT investment to manage that data grew at a far more limited pace. This situation reveals that a very good editing is required for the correct and efficient storage of the obtained data.
• Verification: Another issue that has come to the fore regarding data in recent years is data security and accuracy. Who will display the obtained data and under what conditions, and which of these data should be kept confidential are issues that need to be carefully studied.
• Value: The most important component of big data is value. The data obtained and processed is meaningful as long as it adds value to the organization. For this reason, it should be prioritized that the analysis and simulations of big data are set up correctly and benefit the organization using big data.

If big data is constructed and used correctly, it provides significant benefits in the decision stages of companies and provides them with competitive advantage. Companies that are aware of this situation, benefit from big data in many areas such as marketing, sales and production.

GTech, with big data and advanced analytics services are creating and maintaining the big data infrastructure that your company needs with its expert team and award-winning products. To contact us you can visit contact us page on our website.

Prepared by: GTech Data Science Team

The post What Is Big Data? How to Use It appeared first on GTech.