Editor’s note: This Big Data pipeline article is Part 2 of a two-part Big Data series for lay people. If you missed part 1, you can read it here.
With an end-to-end Big Data pipeline built on a data lake, organizations can rapidly sift through enormous amounts of information. This helps you find golden insights to create a competitive advantage. The following graphic describes the process of making a large mass of data usable.
The steps in the big data pipeline
Understanding the journey from raw data to refined insights will help you identify training needs and potential stumbling blocks:
Organizations typically automate aspects of the Big Data pipeline. However, there are certain spots where automation is unlikely to rival human creativity. For example, human domain experts play a vital role in labeling the data perfectly for Machine Learning. As well, data visualization requires human ingenuity to represent the data in meaningful ways to different audiences.
Additionally, data governance, security, monitoring, and scheduling are key factors in achieving Big Data project success. Organizations must attend to all four of these areas to deliver successful, customer-focused, data-driven applications.
Where do organizations get tripped up?
Here are some spots where Big Data projects can falter:
- Failure to clean or correct “dirty” data can lead to ill-informed decision making. When compiling information from multiple outlets, organizations need to normalize the data before analysis.
- Choosing the wrong technologies for implementing use cases can hinder progress and even break an analysis. For example, some tools cannot handle non-functional requirements such as read/write throughput, latency, etc.
- Some organizations rely too heavily on technical people to retrieve, process, and analyze data. This shows a lack of self-service analytics for Data Scientists and/or Business Users in the organization.
- At times, analysts will get so excited about their findings that they skip the visualization step. Without visualization, data insights can be difficult for audiences to understand.
A lack of skilled resources and integration challenges with traditional systems also can slow down Big Data initiatives.
How can training help?
Training teaches the best practices for implementing Big Data pipelines in an optimal manner. Starting from ingestion to visualization, there are courses covering all the major and minor steps, tools, and technologies. This education can ensure that projects move in the right direction from the start, so teams can avoid expensive rework. Participants learn to answer questions such as:
- How do we ingest data with zero data loss?
- When is pre-processing or data cleaning required?
- What is the process for cleaning data?
- How does an organization automate the data pipeline?
- Which tools work best for various use cases?
- How do you make key data insights understandable for your diverse audiences?
What questions should L&D ask when scoping big data training needs?
Here are some questions to jumpstart a conversation about Big Data training requirements:
- Where does the organization stand in the Big Data journey?
- In what ways are we using Big Data today to help our organization?
- Is our company’s data mostly on-premises or in the Cloud? If Cloud, what provider(s) are we using?
- What are key challenges that various teams are facing when dealing with data?
- What is the current ratio of Data Engineers to Data Scientists? How do you see this ratio changing over time?
- What parts of the Big Data pipeline are currently automated?
- What training and upskilling needs do you currently have? And what training needs do you anticipate over the next 12 to 24 months?
With this information, you can determine the right blend of training resources to equip your teams for Big Data success.
About the author
Bhavuk Chawla teaches Big Data, Machine Learning, and Cloud Computing courses for DevelopIntelligence. As well, he is an official instructor for Google, Cloudera, and Confluent. For the past ten years, he’s helped implement AI, Big Data Analytics, and Data Engineering projects as a practitioner. In his work, he utilizes Cloudera/Hortonworks Stack for Big Data, Apache Spark, Confluent Kafka, Google Cloud, Microsoft Azure, Snowflake, and more. Chawla brings this hands-on experience, coupled with more than 25 Data/Cloud/Machine Learning certifications, to each course he teaches. He has delivered knowledge-sharing sessions at Google Singapore, Starbucks Seattle, Adobe India, and many other Fortune 500 companies.
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