How Do Data Pipelines Fit Into Your Data Stack?
In this article, we'll discuss what a data stack is, how data pipelines fit into and optimize them, and explore pipeline solutions.
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The amount of big data generated around the world by the time you finish this page is limitless. Think about it for a second. Companies everywhere will create an innumerable amount of data right now—customer records, sales orders, chain reports, emails, you name it.
Companies need all this data for data analytics—the science of modeling raw data to uncover precious real-time insights about their business. It's like opening a treasure trove. But there's a problem: Most companies keep data in lots and lots of different places. The average organization draws from over 400 data sources, while 20 percent of organizations have more than 1,000 data sources. And that's a lot.
Some of these data sources are new, and some are old. But because there are so many of them, data analytics becomes rather tricky. What if we could take data from all of these sources and move it to one place for analytics? Doesn't that sound like a much better idea?
Extract, Transform, Load (ETL) does that. It's the most exciting thing to happen to data analytics in decades.
In the simplest of terms, ETL:
- Extracts data from multiple locations.
- Transforms it into usable formats, and
- Loads data into a data store like a data warehouse or data lake.
The entire process is simple, and that's because of data pipelines. These let you process data without breaking a sweat.
Think of data pipelines like the pipes in your home that carry water to your kitchen sink. Data pipelines transport data instead of water, moving it from almost any on-premises or cloud-based data source you can think of to a data lake or a data warehouse like Snowflake or Amazon Redshift. The great thing about data pipelines is that you can build your own, just how you like them, making data analytics even easier.
But how exactly do data pipelines fit into your data stack? What's so great about them? And why should you care? Here's everything you wanted to know about data pipelines but were too afraid to ask.
Tell Me More About Data Pipelines
Data pipelines move raw data from one destination to another destination, transforming it along the way. But there's a lot of things that happen in-between.
Step 1: Data Engineering
Data pipelines pull data from data sources such as:
- Customer relationship management systems (CRMs)
- Enterprise Resource Planning systems (ERPs)
- Sales databases
- Software as a Service (SaaS) solutions
- Legacy systems
And different data types from these data sources flow through data pipelines:
- Structured data: Data that resides in a relational database structure, created using a predefined schema. (Think of tables with cells that contain concrete values.)
- Semi-structured data: Data that doesn't reside within a relational database structure. Semi-structured data contains markers, tags, or other recognized database formats to separate elements within the data. (Think of JSON and CSV files.)
- Unstructured data: Data that's not in a recognized database format. (Think text files, audio files, images, etc.) Read more about unstructured data here.
Data pipelines take data from the above data sources and move it to another destination such as:
- A data warehouse
- A data lake
- A different relational database
Sometimes, data pipelines have more than one destination. (A data pipeline might move data to a data lake and then to a data warehouse, for example.)
Step 2: Data Preparation
Sometimes data isn't ready to go to another destination just yet. Data might be in an unusable format or contain lots of errors. In these scenarios, you need to transform data. Data transformation happens in various ways:
- Data cleansing: Erroneous, duplicate, or null values are removed or amended.
- Data mapping: When you convert data to match the schema of a destination database.
- Data encryption: When you encrypt sensitive data values for data privacy purposes.
- Data enrichment: Data sources merge to create a single source.
- Aggregation: Grouping datasets by specific fields and looking at data in aggregate.
- Limiting: Controlling the number of records in a dataset's output or limiting data on a per-partition or per-group basis.
- Cross-joining: Combining two data inputs.
- Sorting: Pre-sorting data into ascending/descending order.
- Filtering: Filtering data to remove extraneous data.
- SELECT: A transformation component that encrypts, hashes, and masks data. (Used for fields and columns)
- WINDOW: Applying window functions to incoming data, such as creating running totals or ranking data.
Step 3: Accelerating Data Pipelines
Of course, you can't create data pipelines out of thin air. You'll need data pipeline software to facilitate the process. The right software automates various data pipeline tasks so, once you're up and running, you can sit back and let the pipelines do their thing. (The good news is that you can make changes to your data sources and destinations without disrupting pipelines.)
Up until now, creating data pipelines has been a bit of a challenge. Businesses have had to write scripts and applications that pull data from data sources, which is fine for companies with data engineers or data scientists but not so fine for small and medium-sized companies with fewer resources.
Then ETL came along. It processes raw data (with the help of data pipelines) using a three-stage process:
- Extracting data from the source.
- Transforming data into usable formats.
- Loading data to another destination for data analysis.
ETL optimizes data pipelines, but there's still lots of code involved. Not great for all those small and medium-sized businesses.
Then Xplenty came along. This ETL solution boasts over 200 out-of-the-box transformations that make building data pipelines so much easier. Just choose a data source and let the platform pull data into pipelines automatically. There's no code required whatsoever.
How Do Data Pipelines Help Your Data Team?
Data pipelines provide these benefits:
- Moving data from various sources to one centralized destination improves streamlining in your organization.
- Having all your data in one destination improves data analytics. You can use business intelligence tools and algorithms to generate accurate insights into your business for greater decision-making, sales, customer outcomes, and more.
- Data pipelines save time. There's no more wasted time analyzing data in multiple locations. Everything you need is in one place. Benefit from automation.
- Data pipelines enhance data compliance. You can ensure all data is compliant with GDPR, HIPAA, CCPA, and other data governance frameworks.
- Keeping your data in one location improves data security.
- Use cases: Analyzing sales data from legacy systems, preventing fraud, enhancing the customer experience, accessing real-time metrics, etc.
- Ultimately, data pipelines optimize data management.
Data pipelines prove even more effective when you use an ETL solution like Xplenty:
- No code required.
- Easy data transformations that you can incorporate into your workflow.
- Salesforce-to-Salesforce integrations.
- Reduce data latency and data processing.
- Hundreds of data sources and destinations.
- Connect and extract data from any REST API.
- Enhanced functionality.
- Great for scalability.
- Enhanced data security and compliance.
- Affordable pricing.
- Customer support free for all end-users.
Data Pipelines: What's a Data Stack?
A data stack is another name for all the processes associated with moving your data from one location to another location. There are several layers to a data stack, such as:
- Data sources that you currently have that store data. An example of a data source might be your existing CRM system.
- ETL solutions extract data from sources, transform data into readable formats and load data into another destination. An example of an ETL tool is Xplenty.
- A destination database: The place where you want to move data. Examples of destination databases include data warehouses such as AWS Redshift and data lakes such as Amazon S3.
- Business intelligence tools that let you run data analytics from your destination database. An example of a business intelligence tool is Chartio.
Data pipelines are another component of a data stack. Without them, it would be difficult to move data from data sources to a database. It would be difficult to run analytics with business intelligence tools. And it would be really difficult for the ETL process to work properly.
If you want to become a truly data-driven company in 2021, you'll need to invest in a data warehouse or lake, a business intelligence tool, and an ETL solution like Xplenty. Data pipelines will take care of the rest.
Add Data Pipelines To Your Data Stack
Data pipelines are the foundations of your data stack, helping you move data from various locations to a final destination point. You'll improve data security, generate more accurate insights, enhance compliance, and streamline data management in one fell swoop.
Published at DZone with permission of Abe Dearmer. See the original article here.
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