Bigdata - DIANA ADVANCED TECH ACADEMY : Get the World's best IT Courses

Interested in increasing your knowledge of the Big Data landscape?

This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible — increasing the potential for data to transform our world!

BIG DATA IS THE FUEL FOR TODAY'S ANALYTICS APPLICATION

The development of Big data technologies unlocked a treasure trove of information for businesses. Before that, BI and analytics applications were mostly limited to structured data stored in relational databases and data warehouses — transactions and financial records, for example. A lot of potentially valuable data that did’nt fit the relational mold was left unused. No more, though.

Big data environments can be used to process, manage and analyze many different types of data. The data riches now available to organizations include customer databases and emails, internet clickstream records, log files, images, social network posts, sensor data, medical information and much more.

Companies increasingly are trying to take advantage of all that data to help drive better business strategies and decisions. In a survey of IT and business executives from 94 large companies conducted by consultancy New Vantage Partners in late 2021, 91.7% said they’re increasing their investments in big data projects and other data and AI initiatives, while 92.1% reported that their organizations are getting measurable business results and outcomes from such initiatives.

Why is big data important for businesses?

Big data platforms and tools have revolutionized data utilization for organizations. Previously, much of the data was labeled as dark data and remained underutilized. Effective big data management processes enable businesses to harness their data assets, expanding possibilities for analytics. This includes machine learning, predictive analytics, data mining, and more. Big data analytics applications offer benefits like improved marketing, enhanced processes, increased revenue, cost reduction, and stronger strategic planning. Moreover, big data contributes to advancements in healthcare, scientific research, smart cities, law enforcement, and government programs.

What are common big data challenges?

Because of its very nature, big data tends to be challenging to process, manage and use effectively. Big data environments typically are complex, with multiple systems and tools that need to be well orchestrated to work smoothly together. The data itself is also complex, particularly when data sets are large and varied or involve streaming data.
Those issues can be broken down into the following categories:

Technical challenges that include selecting the right big data tools and technologies and designing big data systems so they can be scaled as needed;
Data management challenges, from processing and storing large amounts of data to cleansing, integrating, preparing and governing them;
Analytics challenges, such as ensuring that business needs are understood and that analytics results are relevant to an organizations’ business strategy; and
Program management challenges that include keeping costs under control and finding workers with the required big data skills.
Hiring and retaining skilled workers can be particularly difficult because key contributors such as data scientists, data architects and big data engineers are in high demand.

Key elements of big data environments

Big data management and analytics initiatives involve various components and functions. These are some of their core aspects that need to be factored into project plans upfront.

Big data technologies and tools

The big data era began in earnest when the Hadoop distributed processing framework was first released in 2006, providing an open source platform that could handle diverse sets of data. A broad ecosystem of supporting technologies was built up around Hadoop, including the Spark data processing engine. In addition, various No-SQL databases were developed, offering more platforms for managing and storing data that SQL-based relational databases weren’t equipped to handle.
While Hadoop’s built-in Map-reduce processing engine has been partially eclipsed by Spark and other newer technologies, it and other Hadoop components are still used by many organizations. Overall, the technologies that now are common options for big data environments include the following categories:

Processing engines. Examples include Spark, Hadoop Map-reduce and stream processing platforms such as Flink, Kafka, Samza, Storm and Spark’s Structured Streaming module.
Storage repositories. Examples include the Hadoop Distributed File System and cloud object storage services such as Amazon Simple Storage Service and Google Cloud Storage.
NoSQL databases. Examples include Cassandra, Couchbase, CouchDB, HBase, MarkLogic Data Hub, MongoDB, Redis and Neo4j.
SQL query engines. Examples include Drill, Hive, Presto and Trino.
Data lake and data warehouse platforms. Examples include Amazon Redshift, Delta Lake, Google BigQuery, Kylin and Snowflake.
Commercial platforms and managed services. Examples include Amazon EMR,Azure HDInsight, Cloudera Data Platform and Google Cloud Dataproc.

What are future trends in big data?

Increasingly, organizations are running big data systems in the cloud, often using vendor-managed platforms that provide big data as a service to simplify deployments and ongoing management. Big data trends, moving to the cloud enables businesses to deal with almost limitless amounts of new data and pay for storage and compute capability on demand without having to maintain their own large and complex data centers.

Also listed the following as notable trends:

increasing data diversity, driven in particular by growing data volumes from IoT devices that are leading more organizations to adopt edge computing to better handle processing workloads;
further increases in enterprise use of machine learning and other AI technologies, both for data analytics and to enable chatbots to provide better customer support with more personalized interactions; and
Wider adoption of DataOps practices for managing data flows, as well as a heightened focus on data stewardship to help organizations deal with data governance, security and privacy issues.