If you're a 21st-century business, you've likely considered hiring a data scientist. If you haven't, blame the newness of the field: data science only entered the business lexicon in 2001. That's when William S. Cleveland introduced it as an extension of the field of statistics. Then, in 2009, Hal Varian (Google's chief economist) offered a prescient insight. He proposed that the process of harvesting massive amounts of data and extracting value from it would transform modern business.
He was right. Today, data scientists create machine learning algorithms to solve intricate business challenges. These algorithms help:
- improve predictive fraud capabilities
- identify the motivations and inclinations of consumers down to a granular level. By extension, this helps promote brand awareness, reduce financial burdens, and increase revenue margins.
- predict future customer demand for optimum inventory deployment
- personalize the customer experience
The right data culture is crucial to business success. This is where data pipelines come in. In a nutshell, a data pipeline is a piece of software that enables the moving of raw data from one system to another. The latter, typically, a data warehouse, is where the data is changed into a meaningful format and analyzed.
This last step, analytics, allows businesses to identify market trends and patterns in consumer behaviors. The result is faster, more effective decision-making.
Why You Need A Data Pipeline
First, big data is a goldmine and its possibilities have yet to be fully realized. According to a 2018 McKinsey report, 41% of respondents admit their organizations only began monetizing data in the last two years.
The industries where data monetization is most prevalent? Financial services, tech, and energy.
Businesses in these industries are using enriched customer data to:
- make better marketing and sales decisions
- modify business practices in the supply chain, R & D, as well as capital and workforce management
- add relevant new products and services to existing offerings
- launch new data-focused businesses to differentiate from competitors
- partner with related organizations to share and extract value from harvested data
Meanwhile, C-suite executives are using big data analytics to provide strategic direction to their organizations and to improve operational efficiency.
In all, businesses have leveraged the power of data to realize a 20% increase in revenues. To leverage the power of that data, you'll need a data pipeline.
It's no surprise then, that Gartner identifies augmented analytics and automated insights as one of the top ten technology trends of 2019. Building the right data pipelines is crucial to business agility and growth.
The Two Main Types Of Data Passing Through A Data Pipeline
First, what types of data are organizations already collecting across mobile, web, and native apps?
- Structured Data: data that can be saved and retrieved in a fixed format. This includes device-specific statistics, email addresses, locations, phone numbers, banking info, and IP addresses.
- Unstructured Data: data that's difficult to track in a fixed format. This includes email content, social media comments, mobile phone searches, images, and online reviews.
To extract value from data and grow your business, you'll need to lay down the dedicated infrastructure for data pipelines to migrate data efficiently.
The Elements Of A Data Pipeline
The basic parts of a data pipeline are:
- Sources: where your data originates. This can include data from CRM systems, third-party vendors, flat files, ERP, RDBMS, email lists, and SaaS applications.
- Extraction: data collection from all disparate, siloed sources.
- Denormalization or standardization: discarding extraneous data and prioritizing read-oriented over write-oriented data. The process of denormalization/transformation standardizes the format of all extracted data.
- Loading: after de-normalizing, the data is loaded into a data lake or data warehouse.
- Analytics: data is analyzed using descriptive and predictive analytics. Next, insights are extracted from it. In descriptive analytics, we ask what happened in the past and determine what we can learn. Meanwhile, in predictive analytics, we question what will happen in the future and decide how to adjust our business strategies accordingly.
The entire virtual data pipeline can be automated to facilitate the efficient movement and transformation of data. More importantly, automation also ensures that businesses comply with the provisions of privacy laws.
Today, data pipeline management has evolved beyond the guidelines pertaining to conventional batching. Modern tools, like Xplenty, recognize the complexities of the modern marketplace and allow a wide range of data sources to be stored in data lakes & warehouses, and then analyzed.
The Differences Between A Data Lake and Data Warehouse
While data lakes contain raw data, data warehouses store standardized data. Basically, data warehouses contain processed data that's ready to be analyzed. Business professionals can extract value from standardized data because it corresponds directly to the existing ways they manage data in inter-office spreadsheets.
On the other hand, data lakes contain unfiltered, raw data that's unorganized. This data is less accessible to employees. However, it also holds great potential, in that data scientists can use machine learning analytics to make market-driven recommendations.
Essentially, data lakes allow data scientists to deliver deep insights about complex problems. This, in turn, allows company stakeholders to make more informed decisions.
With Xplenty, businesses can analyze a host of structured, unstructured, and semi-structured data without the need to maintain costly infrastructure.
When You Should Use A Data Lake
- When data needs to be collected for business monitoring and operational reporting
- When you need to combine and manage supply chain file-based data
- Data scientists are available to retrieve important insights from the data
- When analytical queries aren't known in advance
- When the data needs to be made accessible to more stakeholders in an organization
When You Should Use A Data Warehouse
- When you need data that's pre-aggregated and ready to use
- When records and data items must be represented by a schema framework, such as the star or snowflake schema
- When data queries are mostly known in advance
- Exclusive access to specific data must be protected
- When you need to have 100% trust in the reliability of your data
Do You Really Need A Data Scientist to Build Data Pipelines?
Little consensus exists regarding this. At present, data scientists are in hot demand, but no one quite knows what qualifications they should have.
To fill the void, the Open Group (an IT industry consortium) announced three levels of certifications for data scientists in early 2019. To earn the certifications, candidates need to demonstrate knowledge of programming languages, big data infrastructures, machine learning, and AI.
Not too long ago, data scientists were needed to build data pipelines. Today, solutions like Xplenty allow you to build your own pipelines without the need for coding knowledge.
ELT Versus ETL
Although used interchangeably, the two terms are quite different in meaning. First, ETL stands for Extract, Transform, and Load. It differs from ELT systems (note the placement of letters in both acronyms).
In ELT systems, the data is loaded into a central data repository immediately after it's collected. Transformations are only executed after. ELT pipelines work best with Google BigQuery and Amazon Redshift because they enable highly efficient transformations.
Meanwhile, conventional ETL systems collect data from a source, transform it, and then load the data into a data warehouse or end target database. ETL systems run in batches, meaning that the data is processed as a single unit during prescribed intervals.
By contrast, "data pipeline" is a general term that includes ETL and ELT. It refers to a system for moving data from one system to another. The data may be processed either in batches or in real time.
ELT/ ETL pipelines, data lakes, and data warehouses make up the elements of big data architecture.
Big Data Architecture
Today, the most popular big data architectures are the Lambda and Kappa architectures.
Lambda architecture comprises a Batch Layer, Speed/Stream Layer, and Serving Layer. It is, in a nutshell, a system of dividing data systems into "streaming" and "batch" components. Lambda is a hybrid system that can process both OLAP (analytical) and OLTP (transactional) applications.
Gartner refers to Lambda as an HTAP (Hybrid Transaction/ Analytical Processing) system. HTAP consists of Point-of-Decision HTAP and In-Process HTAP.
Point-of-Decision HTAP is a data architecture that deploys in-memory computing (IMC) to enable simultaneous analytic and transaction processing. Meanwhile, In-Process HTAP combines both analytic and transaction processing to deliver real-time, hyper-personalized UX (user experience).
For its part, Kappa architecture is a version of Lambda with batch processing disabled. Both Lambda and Kappa have their advantages. However, their collective disadvantages may prove daunting to business professionals who want to build their own data pipelines.
For Lambda, the operational challenge of debugging two disparate systems may prove insurmountable. Additionally, the problem of data reprocessing can't be ignored. Meanwhile, Kappa isn't a one-size fits all solution, despite its streaming capabilities.
Should You Build Your Own Data Pipeline?
It depends. Before you begin, consider these challenges:
- Situations where the source and target schemas don't match. Incompatible schemas can negatively affect the efficacy of your pipeline.
- Scenarios where the data pipeline infrastructure can't support increasing data volumes and the evolution of data sources.
- Situations where your pipelines can't support both batch and stream processing.
A good data pipeline is efficient, reliable, and effective.
No one knows that better than the team at Xplenty. If you're looking to build an efficient low-latency pipeline, our intuitive graphic interface offers many benefits. Xplenty's pre-built integrations allow you to connect various endpoints, like Google Analytics, AdWords, Redshift, BigQuery, and Salesforce, without writing any code. You can get up and running in minutes and your destination needn't be a data warehouse. For more information about making your business more agile and profitable, contact us or schedule a demo.