Batch processing

What is batch processing?

Batch processing involves processing large volumes of data within stipulated time intervals using automated agile workflows and less human interference. Big data needs to be processed in batches when the data is voluminous and the processed data is not needed in real-time.

Batch processing would involve job scheduling based on the time allocated for the job to run.

What is job scheduling?

Job scheduling involves allocating specific time intervals for processing your data batches. It is an automated resource that determines what job to execute and at what time.

Intervals could be available :

• By minutes

• Hourly

• Daily

• Weekly

• Monthly

You have the decision to determine the intervals your job or data batch should be run.

Requirements for batch processing

For batch processing to be carried out, the following must be in place:

• A large amount of data: Data with heavy sizes are best processed in batches to optimize computational resources.

• Repetitive process: If the requirement for processing a large amount of data is to be done regularly, it is best to process it in batches. This helps eliminate human errors or bias in the process and saves time.

Why is batch processing necessary?

• Speedily process big data: Large amounts of data are processed speedily and efficiently with automated systems.

• Eliminate human bias or errors: The exclusion of human interference makes the work gets done faster and eliminates human bias or errors which can affect the credibility of the processed data.

• Manage computational resources: With big data available, it is best to process in batches within the computational strength of the resources available for good results to be churned out.

• Run batches at any time asynchronously: Batches can be run at any time interval based on the stipulations placed. You can set your interval and go ahead with other activities.

• Reduce operational cost: Less human oversight is required which cuts down cost on manpower and also pricing runs based on time allocated for the batches to be run. This helps to save costs.

ETL

What is ETL?

ETL which stands for Extract Transform Load is a simplified combination of a three-step process in data processing with a well-suited infrastructure in place that eliminates technicalities and saves time.

The processes involved in processing big data are simply generalized into 3 steps

• Extract: Gathering data from various sources into a single place

• Transform: Converting data into a valuable format

• Load: Taking processed data to the final procedure for use.

These are 3 heavy processes that automation solves with ETL pipelines that seamlessly help to integrate data from various sources and transform it speedily before loading.

Benefits of ETL

• Seamless ingestion: Data stored in disparate sources are easily assembled for transformation

• Speedy data transformation: Data transformation which on a normal day, requires complex tasks and a lot of time is carried out speedily with an ETL pipeline

• Data lineage- ETL pipelines make room for the historical contexts of each process a data batch has gone through to be tracked.

• Unified data: Data in scattered locations can not be made sense of. ETL pipelines make it possible for data sources to be combined so insights can be sourced out of them.

• Simple overview: With ETL pipelines, big data processing is slicker. Non-technical team members can understand data transformation from the simple overview of dragging and dropping data for transformation.

• Competitive advantage: Enterprises gain a competitive advantage with data-driven decisions. Also, they spend less time handling their data and focus on upscaling efficiency.

Use Cases of ETL

• Clean organizational data for insights

Worried about how to seamlessly get your enterprise data unified and clean? An ETL pipeline is well-suited for this problem. Securely plug your data into one source and easily track all transformation processes for authoritative business reports.

• Manage multiple data sources and formats with ease

Laid back by big data redundant in so many places? ETL proffers the best way for you to easily manage your enterprise data by unifying it for transformation and loading. Hence, you have access to unified data processed and pre-processed.

Managed compute cluster

What is a Managed Compute cluster?

Clusters refer to a combination of virtual machines or nodes that are tasked with the responsibility of handling all the data transformation jobs.

Clusters are in various capacities (e.g. Light job cluster for light weighted jobs)on the demand of your job type. You can select the range of machines to be assigned to your workload per time without manually scaling them.

Autoscaling

Auto-scaling is a computational feature that automatically adjusts the clusters that handle a job at a time.

Benefits of autoscaling

• Save cost: With the auto-scaling feature, you optimize the machines per time hence you don’t get to pay for machines with an exceeded work rate.

• Save time: Human interference is scrapped out to manually scale the machines per time which lets the work get done faster.

• Upscale efficiency: Data teams are made to be efficient by focusing on other tasks rather than constantly reviewing machines.

Benefits of Managed Compute cluster

• Automated tasks- Each machine is tasked automatically with specific tasks per time

• Simple management- You can choose your clusters, clone and delete based on your task demand.