In the world of business, this means that traditional analytics tools have become incapable of handling such data. Hence, the need for easily-scalable and cost-effective tools with larger compute capacities is a reality that business owners have to contend with. As data grows by volume and complexity, big data analytics platforms such as Microsoft Azure, Cloudera, Google BigQuery, Oracle, AWS, and others have developed solutions to remedy the data analysis dilemma. As such, AWS big data training and others are essential for professionals who handle big data.
What is big data?
Big data is everywhere and is today easily accessible by businesses, governments, science and research institutions, various industries, and even individuals. Big data is defined in terms of its:
- Volume – Big data volume ranges from terabytes to petabytes, and by the end of the year 2020, zettabytes.
- Velocity – The speed with which data is being generated from various sources and how fast this data can be accessed.
- Variety – Data can be structured or unstructured and still come in several different formats, for instance, documents, text messages, emails, video, audio, images e.t.c. This presents one of the biggest challenges of big data processing because the sooner it is analyzed the more value it offers. The ideal is analysis in real-time which is often not achievable for some businesses.
Big data has also been defined using other ‘Vs’ like variability, value, and veracity. A good big data strategy will help companies to reduce operating costs while at the same time increasing their operational efficiencies and competitiveness significantly.
Requirements for big data analytics
To achieve efficiency, big data analytics require environments that can support:
- Data processing
The data processing cycle encompasses the collection, preparation, input, processing, output, and storage. Analytics tools should have the computing power to process big data in addition to cloud processing capabilities to take care of spikes in demand.
- Batch processing
In batch processing, data is collected over time and then processed in blocks. The open-source Hadoop MapReduce was developed for this purpose. Batch processing use cases include payroll and customer order processing and would typically have time windows of hours and in other cases days.
- Stream processing
Stream processing, also known as event stream processing, has been necessitated by the high velocity and wide variety of big data. Real-time data processing makes sense in many occasions for instance the stock market, business analytics, fraud detection, and systems monitoring. Tools like Apache Spark, Apache Kafka, Amazon Kinesis have been built to facilitate stream and real-time data processing.
- Predictive analytics
Data modeling represents complex data sets in simple easy-to-understand visuals while data mining involves discovering hidden patterns in data sets which are then used to make decisions. Predictive analytics uses both stored and real-time data to gain futuristic insights about their customers and the market.
- The cloud resource
Among the most common uses of the cloud is big data processing. People prefer the cloud platform because it is elastic thus scaling up or down is fast, easy, and in most cases does not cost anything. Secondly, the cloud features robust computing power that supports demanding tasks like stream processing. Top service providers like AWS have different services on offer that allow enterprises to get the most out of big data.
Reasons to learn Big Data on AWS
Processing and analyzing big data requires high-performance computing power in an environment that can support unexpected demand (both vertical and horizontal scaling) easily without having to make additional software and hardware investments. AWS, the leading cloud service provider, offers the widest range of products and services that allow businesses to build high-performance cloud systems to leverage big data. AWS uses the cost-effective pay-as-you-go billing model to support on-demand up and down scalability.
AWS offers over 175 services including servers, storage, networking, computing, analytics, AI, IoT, application development, and many more in more than 76 Availability Zones within 24 geographic regions. AWS platform features reliable security features which have made AWS the most preferred service provider by start-ups, small and medium-sized businesses, large companies, and government institutions.
AWS big data services
AWS offers the following big data products and services
1 – Amazon Kinesis. A data streaming service for real-time collecting, processing, and analysis of streaming data. Kinesis has the capacity to capture gigabytes of data per second continuously from thousands of sources. This tool ingests data in various formats such as video, audio, web clickstreams, IoT data, and more and will process and analyze data in the form it arrives in at any scale.
2 – AWS Snowball. This is a data transfer solution that facilitates the migration of bulk data from on-premise storage to AWS S3 buckets and vice versa. Snowball service takes care of security and transfer speed concerns.
3 – Amazon Simple Storage Services (S3). Amazon’s storage system is ten times larger than its closest competition. S3 hosts websites for big names like Netflix, Spotify, Airbnb, and Instagram. Amazon S3 provides highly scalable, low latency, and highly secure data storage on the cloud. Its simple web service interface, businesses can store and access data easily while also replicating your data automatically to prevent loss of data.
4 – Amazon Glazier Archival Storage. Now known as Amazon S3 Glacier, this is a backup and as its name suggests, an archival storage facility, mostly used by Amazon’s S3 clients to store data that they do not access frequently. The S3 Glazier offers storage at a relatively low cost compared to that of Amazon S3.
5 – Elastic Compute Cloud (EC2). One of AWS’s most popular services, EC2 is a virtual machine service that allows businesses to run their applications on AWS cloud and assume full control of their (applications) computing resources. EC2 is highly secure and features an automated resizable compute capacity with extended storage for processing large data sets to allow for easy scaling. Compared to its competition, EC2 flaunts seven times fewer downtime hours and more than 300 VM instances.
6 – Amazon Elastic MapReduce (EMR). Amazon EMR uses Hadoop and Spark frameworks to process large volumes of data efficiently. EMR also includes managed EMR notebooks for data science and data engineering projects.
7 – Amazon relational database service (RDS). Scalable relational database service that lets businesses set-up, run, and scale relational databases in the AWS cloud. It offers a scalable capacity as well as the management of common database administration tasks.
8 – Amazon DynamoDB. A NoSQL database that supports document storage in key-value and document structures. This tool offers high performance and elasticity for easy scalability. It comes with in-memory caching, built-in security features, as well as backup and restore functions.
9 – Amazon Redshift. Redshift is Amazon’s data warehouse facility. It is available in the cloud and features large petabyte-scale storage that allows for the storage of data in columns and parallel queries. The Redshift Spectrum function runs SQL queries against both structured and unstructured data stored in S3.
10 – Amazon QuickSight. This is a service that allows users to build visualizations and dashboards in the AWS cloud.
In conclusion
AWS offers much faster, scalable big data processing and analysis services. AWS is preferred because it offers users a wide range of services and the ability to provision more storage and compute capacity seamlessly on the cloud. It is widely and immediately available in several geographic regions and happens to be the most secure cloud platform. With 90% of companies on the cloud and AWS commanding a 32% market share, big data professionals should consider learning big data on AWS.