Data is an inevitable business resource now to do proper planning and operations management to ensure success. So, as the need for proper database management to ensure that the data is stored and tuned to attain this success. This importance for data management had even made many of the corporate firms to pause their current operations and then restructure on the data analytics part to optimize the performances.
With more and more enterprises, ranging from startups to industry leaders, wake up to this insightful understanding of data and its impact on business success, there has been an increasing demand for quick data streaming and processing. Various applications and business requirement may change, and when it comes to BigData, the streaming should be made capable of delivering the best results at real-time.
Consider a financial or banking services business calculating the EMI of various loan products or the mutual fund returns etc. That sort of a website will take needed data and give the users results based on the latest market conditions, which should be derived from tons of details to provide the best-informed decision. However, for the end user, all these happen in just a fraction of second when they see the output. This process is called data streaming.
Avoiding the technical jargons, data streaming is primarily the process of handling huge Big Data sets simultaneously to deliver the most accurate results in a matter of just seconds. As a result of effective data streaming the query makers get updated and real-time information about what they look for to make better-informed decisions.
As the name suggests, data is in constant motion in data streaming, which further gets the process over various server clusters before finally gets stored to the database. This stream of data is sent in different kilobyte size chunks and gets processed per record. Analytical processes also happen simultaneously, and tons of sampling, filtering, and aggregations may have happened by the time the end user received the needed set of data. Considering this flow, the major element of data streaming to decide its effectiveness is speed.
Some major data types processed using streaming techniques are:
- Website and mobile apps user log files.
- Internet gaming activities.
- Social media profiles and feeds (Facebook, Instagram, Twitter, LinkedIn, etc.)
- Online store products and purchases.
- Geospatial applications.
- Finance and banking applications
- Information sharing in IoT ecosystems
For a better understanding of the concept, as explained by RemoteDBA.com, you may imagine that you sing up with a video streaming portal. When you sign up, you can log in through your Facebook account and complete the procedure. While you sing up to this way, you can find some flicks which may show different languages on your feed along with the most trending movies or TV series, etc.
Before you reach the next page, there are chunks of operations performed at the back end to give you the necessary information. During this process, the new portal you logged into had actually tracked and gathered innumerable pieces of information from the Facebook in order to get your geographical location, personal demographics, languages, and various other info. The new site may have collected various personal info like your interests, what you liked, your feeds, topics you posted, photos, places visited, and even the celebrities you follow the most and so much more.
Data Streaming vs. Batch Processing
Apart from the element of speed which we discussed above, another major difference between these two data management concepts is that batch processing actually takes a huge chunk of data to give the most aggregated output for in-depth analysis. However, data streaming only considers smaller data fragments otherwise called micro-sets, which deliver much more efficient results.
For example, a batch processing may be more appropriate when the HR is analyzing the attrition rates in the company or the level of employee satisfaction among various departments while working on the appraisals and incentives. In this case, you can note that the data volume fed into each process is huge and to be processed for the entire inference. On the other hand, if the HR needs to try out data streaming, it could be better used for recruitment, in which a potential candidate can be tested instantly to asses whether the applicant will be committed to the job or not.
In technical terms:
- Batch processing actually works based on queries from various data sets, whereas
- Data streaming is more relying on individual records or the recent data sets.
- Batch processing latency may range from a minute to many hours based on the intensity, whereas
- Data streaming latency may be just a few seconds, if not milliseconds.
- Many complex analytics algorithms are working in data processing in batch processing, whereas
- Only simple operations as rolling metrics, response functions, aggregation are used in data streaming.
Data streaming challenges
Even though we can find many benefits in adopting data streaming models, like any other technological features, one can find some challenges also for Big Data specialists when it comes to data streaming. One critical challenge which defines the entire data streaming process is the speed next to its built.
Data streaming demands two unique operational layers as the:
- Fundamental storage layer
- The processing layer.
Role of the data storage layer is to record the data provided by users and just facilitate faster and seamless data relays on to the processing layer. The processing layers are responsible for absorbing data from the storage layer and perform all analytical and computations tasks on the data set and again notify the storage layer to delete any data which is not needed.
All these activities need to be accomplished in milliseconds to ensure the desired results. Apart from all these, there are further challenges too in terms of fault tolerance, scalability, and durability of data. Considering your unique requirement in hand, Big Data admins need to plan effective data streaming or batching methodologies for better management of their data to effectively make use of it for business success.
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