These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. 

Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.  

  1. What Is a Data Stack?
  2. Who Benefits from a Data Stack? 
  3. ETL: The Engine of the Data Stack
  4. Examples of Data Stack Components 
  5. How to Implement Xplenty in Your Data Stack

What Is a Data Stack?

A data stack or analytics stack is a collection of systems that make up one pillar of your infrastructure. For example, your marketing stack might include a CRM, marketing automation, and analytics tools. Information flows through a stack according to your internal processes. This allows your people to access what they need when they need it. 

A data stack is made up of all the systems required to facilitate a smooth data journey from inception to disposal. Analytics stacks are useful for systems integration and analytics. They also help separate computational power from information storage, which improves efficiency.

Your data stack can comprise as many elements as you need. These elements will fall roughly into the following categories: 

  • Source: The live systems that generate data. This may include things like your CRM, eCommerce system, ERP, MySql databases, and systems that automatically generate logs and reports. These are typically live systems with lots of write activity on the database. 
  • Pipeline: Pipelines are automated processes that take data from one location and push it to the next. Pipelines enable system integration, as well as data consolidation. 
  • Transformation layer: You'll often need to convert data when it's moving between databases. This happens in a dedicated database known as the transformation layer. Data only stay here for a short while before being converted into an appropriate format and sent to its destination. 
  • Storage: There are several storage options, depending on your data needs. Data warehouses store structured data, data lakes handle all kinds of data, and data marts show a department-specific data view. All of these solutions are cost-efficient ways to store data at scale, especially when you use a cloud service. You can also consolidate multiple data sources into a single storage repository. 
  • Analytics: Data analytics and business intelligence tools can connect to your storage repositories. You can use these tools to get a detailed insight into your organization's current state. 

Theoretically, data can flow through this process in an instant which gives businesses access to real-time and streaming analytics. However, that all depends on the structure of your data stack. You need the right components in place to realize the benefits of full data integration. 

Customer Story
Customer Story
Keith connected multiple data sources with Amazon Redshift to transform, organize and analyze their customer data.
Amazon Redshift Amazon Redshift
David Schuman
Keith Slater
Senior Developer at Creative Anvil
Before we started with Xplenty, we were trying to move data from many different data sources into Redshift. Xplenty has helped us do that quickly and easily. The best feature of the platform is having the ability to manipulate data as needed without the process being overly complex. Also, the support is great - they’re always responsive and willing to help.
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Who Benefits From a Data Stack? 

Data is the lifeblood of any organization. Everything is measurable these days, from financial activity to employee productivity. All of this data is potentially valuable. 

A stack is a way of corralling all of that wild information and bringing it into a single location. With transformation technology, you can even wrangle data into a single format. That's incredibly useful for analytics, but it helps other departments too: 

  • Operations and Customer Service: An organized analytics stack helps all of your systems to talk to each other. The record stored in the data warehouse can act as a master record or a Single Version of Truth. This helps eliminate inconsistencies and streamline processes, ultimately leading to a more satisfying customer experience. 
  • Marketing: Visibility is essential for marketing. It's not just sales funnel journeys, but all data about customer interactions and market performance. An efficient data stack will allow the marketing team to improve conversions. It may even help them identify opportunities for new products or services. 
  • Management: Data-driven decision making is a standard procedure for most large organizations. Senior leadership members need accurate, detailed insights to help them implement a strategy. Leaders also need access to live dashboards and visualization that allow them to monitor their current state. For this, they may use BI tools like Looker and Chartio, which require a steady stream of data.
  • Compliance: A well-organized data stack can make life much easier for the compliance team. Data warehouses allow you to sensitive information securely at scale. You can also consolidate everything in a single repository. A good data governance policy is essential for optimal compliance. 
  • Analytics: It's worth reiterating that the analytics team is entirely dependent on the tech stack. Each part of the data stack impacts their ability to produce timely and accurate insights from their data sets.

Every department depends on the data stack in some way. Think of it as the central pillar around which your enterprise is built.  

ETL: The Engine of the Data Stack

At the heart of every data stack is an algorithm. This algorithm pumps data through the data pipeline. It gathers information, processes it according to specifications, and delivers it to the desired destination.

The most common version of this algorithm is Extract, Transform, Load, or ETL. There are three discrete steps in the process: 

  • Extract: The ETL takes data from each of the sources, typically by performing API calls. This might be a full data export or just the changes since the last export. 
  • Transform: In a dedicated staging layer, the ETL process transforms data. Typically, transformations include data quality checks, error handling, formatting, cleaning, and reshaping. This step will also convert information into the destination data structure. 
  • Load: Once the data is in the right format, the ETL will send everything to its ultimate destination. Typically, this is a data warehouse or data lake. 

ETL has been around since the early days of database technology, with most organizations creating their own in-house tools. 

Today, most companies rely on cloud-based ETL to provide speed, accuracy, and security. No-code ETL like Xplenty doesn't even require technical expertise. You can simply use the visual interface to create the integrations you need and set your pipeline running. 

As data structures have evolved, so too has ETL. Other approaches include: 

  • ELT: Extract Load Transform doesn't have an intermediate transformation layer. The advantage of this is that you get data to its destination much faster. The trade-off is that you have to perform pre-analytics transformations later in the process. This is possible if you're using a repository such as a data lake, which supports unstructured data. 
  • ETLT: Extract Load Transform Load is a compromise solution. The system extracts data and then performs minor transformations, such as data masking. It then pushes everything through to the repository. As with ELT, you then perform on-demand transformations as required. 

Each of these approaches supports a different use case. The most important thing is to consider the needs of your data users and to choose an approach that suits them. 

Examples of Data Stack Components 

There are endless potential configurations of analytics stacks on the market. Let's take a look at some of the most popular components that appear repeatedly in enterprise stacks. 

Best Data Sources

Any software system that generates data has the potential to be a data source. In most organizations, you'll start with core applications like: 

  • Customer Relationship Management (CRM)
  • Enterprise Resource Planning (ERP)
  • eCommerce and ordering systems
  • Financial systems
  • Customer self-service tools
  • Payroll and HR systems
  • Website analytics
  • Automation tools, such as marketing and email automation
  • Output from IoT devices
  • System logs

The best data sources are those that offer a well-designed API. An API is a simple programmable interface that allows you to request data without exposing the underlying database. Most modern cloud-based systems allow data extraction by API.

The pioneer in this space is Salesforce, which released the first-ever public API. Today, Salesforce offers an astonishing level of integration with other systems. Their platform allows a data-first approach to sales and marketing. 

Customer Story
Customer Story
Keith connected multiple data sources with Amazon Redshift to transform, organize and analyze their customer data.
MongoDB MongoDB
Amazon Redshift Amazon Redshift
David Schuman
Dave Schuman
CTO and Co-Founder at Raise.me
They really have provided an interface to this world of data transformation that works. It’s intuitive, it’s easy to deal with [...] and when it gets a little too confusing for us, [Xplenty’s customer support team] will work for an entire day sometimes on just trying to help us solve our problem, and they never give up until it’s solved.
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Best Data Pipelines

Xplenty is the market leader in the ETL space for a number of reasons, such as:

  • Huge integration library: Xplenty is pre-configured to work with most RESTful APIs. This means that you don't have to hand-code an interface. Simply pick your source from the menu, provide the key, and you're good to go. 
  • No-code data pipeline: You also don't require database expertise, as you can use the visual interface to create mappings. These mappings are the heart of your pipeline. Raw data arrives in, and Xplenty automatically transforms it according to your schema. 
  • Field-level encryption: Data in transit is extremely vulnerable. Xplenty offers field-level encryption for sensitive data. During transit, no one can decrypt it without the relevant key. 

Security is a major concern in any data stack, especially during the ETL stage. Xplenty offers a range of security features, including excellent physical security and full SOC 2 compliance. 

For more information about how data pipelines fit into your data stack, click here.

Best Data Warehouse

Amazon's AWS offers a host of cloud computing services, including the extremely popular Amazon Redshift. Redshift is a competitively priced warehousing solution that suits most enterprise needs. 

Data warehouses handle structured data, or data stored in SQL tables. They're the perfect endpoint for an ETL process. You can use the transformation layer to change incoming data to fit within the warehouse data structure. 

Best Data Lake

Microsoft Azure offers one of the better enterprise implementations of a data lake. It's versatile, well-supported, and a familiar progression for companies that have used other Microsoft database products. Google BigQuery is also a popular alternative, while those who prefer open-source options may look at MongoDB.

Data lakes don't require a data structure, so you can use them to store anything you need. As a result, they're better suited to an ELT process without an intermediate transformation layer. 

How to Implement Xplenty in Your Data Stack

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Xplenty allows you to set up a sophisticated data stack, even if you don't have a lot of in-house expertise available. But how do you get started with Xplenty? 

Here are a few steps to help you reinvent your approach to data: 

  1. Think about data consumers. Who will be the main stakeholders here? Remember, it's not only IT and analytics who are dependent on data. Every department has a say in how you build your data infrastructure. 
  2. Look at available sources. Catalog all the available data sources within the organization. New systems may create additional data. For instance, hardware with IoT sensors may generate detailed data logs.
  3. Categorize and prioritize data. Not all data needs to flow through your pipelines. Appraise all of your data in terms of its potential operational value. 
  4. Implement a data governance policy. Every company needs a unified approach to data governance. This policy will help to identify security and compliance issues before they arise. It will also ensure a consistent approach to data throughout the organization. 

Once you're ready to take your next steps, get in touch with Xplenty. Contact our support team to schedule a demo or get a risk-free, 14-day pilot and experience the Xplenty platform for yourself.