An Introduction to Big Data Tools for Beginners
bigdata is a buzzword lately, but how exactly can we as programmers create homemade solutions by implementing this concept? Let’s look!
Data is a collection of facts.
Data qualified as “big” in at least one of the following aspects is called Big Data:
- Volume — The amount of data being large
- Variety — A wide range of data types or values
- Velocity — High-speed incoming data
Now that we have a basic understanding of what these previously mentioned buzzwords mean, let’s dig deeper into the set of tools we can use to help us create solutions for the people around us using the Big Data!
One of the qualities of Big Data is its volume, and storage becomes a big problem if it is not dealt with quickly and efficiently. Of course, there are a wide range of resources to solve this problem using the power of the cloud!
1. The Apache Suite
Apache provides us with a wonderful set of open source software tools for
Apache Hadoop is a framework for both distributed storage and processing large amounts of data. He makes use of the HDFS (Hadoop Distributed File System) for storage.
Apache Cassandra is a large-scale database system, which focuses on 24 hour data availability. It is especially useful for organizations that cannot afford data loss/inaccessibility in the event of system failure.
MongoDB is one of the most popular NoSQL database systems on the market. It can be integrated into web-based appson the Java Platform or the .NET platformall of which contribute to its popularity.
3. Google Cloud Platform
The GCP offers a wonderful pair of services for cloud data storage. Tasks become much easier when processing and storage take place in one place!
A large-scale cloud solution data warehouse for relational structured data and its analysis.
- Big table
A large-scale cloud solution NoSQL database suitable for heavy read-write workloads.
4. The AWS Suite
Amazon Web Services provides one of the brightest big data and cloud platforms for large organizations.
- Amazon Simple Storage Service
Amazon S3 provides a solid cloud storage infrastructure in the form of S3 Buckets that contain data as objects. S3 can be connected to almost any AWS product and is extremely easy to use.
- Amazon RedShift and RedShift Spectrum
Cloud-based storage services for relational structured data. Often used for store processed and cleaned data to be used for analytics, it has the ability to connect to a number of AWS products to fast data transfer and analysis.
- Amazon Elastic Card Discount
EMR is the cluster management service provided by AWS. It is inspired by Hadoop for its special distributed file storage systemand also helps autoscale cluster sizes to accommodate less traffic during happy hours and higher traffic during peak hours.
The data stored in the databases is of no use unless it is processed and used to obtain valuable business information. This is where Big Data Processing comes in.
1. The Apache Suite
- Apache Spark
Like its companion platform Hadoop, Spark offers large-scale data processing, often embedded in applications using programming languages like Python!
- apache storm
Offers data processing such that each unit of data is processed at least once, and nodes automatically restart on failure, ensuring accurate processing.
- Apache Kafka
Makes it easy to process billions of event triggers in a day. Useful for streaming and processing streaming data, for example, analyzing site clickstream data!
2. Quick Miner
One of the famous open source data processing and analysis tools, which connects to both internal and cloud databases. Apart from data processing, also facilitates the creation of machine learning models for predictive analysis.
3. The Microsoft Azure platform
Microsoft has been the pioneer in the field of cloud services with its revolutionary inventions and currently offers one of the largest cloud platforms for big data and its related fields.
- Azure Synapse
Synapse is one of the newest and most versatile big data analytics services on the Azure Cloud Platform. It has a full set of products to integrate with each other and orchestrate an entire data pipeline!
- Azure Databrick
Offers an Apache Spark-based analytics platform for the Azure Cloud System.
- Azure Stream Analytics
As the name suggests, it makes it easy to stream and process streaming data, even for large data.
Please note that these are only a handful hundreds of viable, brilliantly orchestrated and useful platforms for big data.
Even if I tried to recruit them all, it would never be enough, because the ever-growing industry will bring new and better solutions! Such is the beauty of technology!
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Thanks for the patient reading!