9 Helpful Tools for Building a Data Pipeline

Companies create tons of disparate data throughout their organizations through applications, databases, files and streaming sources. Moving the data from one data source to another is a complex and tedious process. Ingesting different types of data into a common platform requires extensive skill and knowledge of both the inherent data type of use and sources.

Due to these complexities, this process can be faulty, leading to inefficiencies like bottlenecks, or the loss or duplication of data. As a result, data analytics becomes less accurate and less useful and in many instances, provide inconclusive or just plain inaccurate results.

For example, a company might be looking to pull raw data from a database or CRM system and move it to a data lake or data warehouse for predictive analytics. To ensure this process is done efficiently, a comprehensive data strategy needs to be deployed necessitating the creation of a data pipeline.

What is a Data Pipeline?

A data pipeline is a set of actions organized into processing steps that integrates raw data from multiple sources to one destination for storage, business intelligence (BI), data analysis, and visualization.

There are three key elements to a data pipeline: source, processing, and destination. The source is the starting point for a data pipeline. Data sources may include relational databases and data from SaaS applications. There are two different methods for processing or ingesting models: batch processing and stream processing.

  • Batch processing: Occurs when the source data is collected periodically and sent to the destination system. Batch processing enables the complex analysis of large datasets. As patch processing occurs periodically, the insights gained from this type of processing are from information and activities that occurred in the past.
  • Stream processing: Occurs in real-time, sourcing, manipulating, and loading the data as soon as it’s created. Stream processing may be more appropriate when timeliness is important because it takes less time than batch processing. Additionally, stream processing comes with lower cost and lower maintenance.

The destination is where the data is stored, such as an on-premises or cloud-based location like a data warehouse, a data lake, a data mart, or a certain application. The destination may also be referred to as a “sink”.

Data Pipeline vs. ETL Pipeline

One popular subset of a data pipeline is an ETL pipeline, which stands for extract, transform, and load. While popular, the term is not interchangeable with the umbrella term of “data pipeline”. An ETL pipeline is a series of processes that extract data from a source, transform it, and load it into a destination. The source might be business systems or marketing tools with a data warehouse as a destination.

There are a few key differentiators between an ETL pipeline and a data pipeline. First, ETL pipelines always involve data transformation and are processed in batches, while data pipelines ingest in real-time and do not always involve data transformation. Additionally, an ETL Pipeline ends with loading the data into its destination, while a data pipeline doesn’t always end with the loading. Instead, the loading can instead activate new processes by triggering webhooks in other systems.

Uses for Data Pipelines:

  • To move, process, and store data
  • To perform predictive analytics
  • To enable real-time reporting and metric updates

Uses for ETL Pipelines:

  • To centralize your company’s data
  • To move and transform data internally between different data stores
  • To Enrich your CRM system with additional data

9 Popular Data Pipeline Tools

Although a data pipeline helps organize the flow of your data to a destination, managing the operations of your data pipeline can be overwhelming. For efficient operations, there are a variety of useful tools that serve different pipeline needs. Some of the best and most popular tools include:

  • AWS Data Pipeline: Easily automates the movement and transformation of data. The platform helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available.
  • Azure Data Factory: A data integration service that allows you to visually integrate your data sources with more than 90 built-in, maintenance-free connectors.
  • Etleap: A Redshift data pipeline tool that’s analyst-friendly and maintenance-free. Etleap makes it easy for business to move data from disparate sources to a Redshift data warehouse.
  • Fivetran: A platform that emphasizes the ability to unlock faster time to insight, rather than having to focus on ETL using robust solutions with standardized schemas and automated pipelines.
  • Google Cloud Dataflow: A unified stream and batch data processing platform that simplifies operations and management and reduces the total cost of ownership.
  • Keboola: Keboola is a platform is a SaaS platform that starts for free and covers the entire pipeline operation cycle.
  • Segment: A customer data platform used by businesses to collect, clean, and control customer data to help them understand the customer journey and personalize customer interactions.
  • Stitch: Stitch is a cloud-first platform rapidly moves data to the analysts of your business within minutes so that it can be used according to your requirements. Instead of focusing on your pipeline, Stitch helps reveal valuable insights.
  • Xplenty: A cloud-based platform for ETL that is beginner-friendly, simplifying the ETL process to prepare data for analytics.

 

How We Can Help

Building a data pipeline can be daunting due to the complexities involved in safely and efficiently transferring data. At 2nd Watch, we can build and manage your data for you so you can focus on BI and analytics to focus on your business. Contact us if you would like to learn more.

Simple & Secure Data Lakes with AWS Lake Formation

Data is the lifeblood of business. To help companies visualize their data, guide business decisions, and enhance their business operations requires employing machine learning services. But where to begin. Today, tremendous amounts of data are created by companies worldwide, often in disparate systems.

These large amounts of data, while helpful, don’t necessarily need to be processed immediately, yet need to be consolidated into a single source of truth to enable business value. Companies are faced with the issue of finding the best way to securely store their raw data for later use. One popular type of data store is referred to as a “data lake, and is very different from the traditional data warehouse.

Use Case: Data Lakes and McDonald’s

McDonald’s brings in about 1.5 million customers each day, creating 20-30 new data points with each of their transactions. The restaurant’s data comes from multiple data sources including a variety of data vendors, mobile apps, loyalty programs, CRM systems, etc. With all this data to use from various sources, the company wanted to build a complete perspective of a CLV and other useful analytics. To meet their needs for data collection and analytics, McDonald’s France partnered with 2nd Watch. The data lake allowed McDonald’s to ingest data into one source, reducing the effort required to manage and analyze their large amounts of data.

Due to their transition from a data warehouse to a data lake, McDonald’s France has greater visibility into the speed of service, customer lifetime value, and conversion rates. With an enhanced view of their data, the company can make better business decisions to improve their customers’ experience. So, what exactly is a data lake, how does it differ from a data warehouse, and how do they store data for companies like McDonald’s France?

What is a Data Lake?

A data lake is a centralized storage repository that holds a vast amount of raw data in its native format until it is needed for use. A data lake can include any combination of:

  • Structured data: highly organized data from relational databases
  • Semi-structured data: data with some organizational properties, such as HTML
  • Unstructured data: data without a predefined data model, such as email

Data Lakes are often mistaken for Data Warehouses, but the two data stores cannot be used interchangeably. Data Warehouses, the more traditional data store, process and store your data for analytical purposes. Filtering data through data warehouses occurs automatically, and the data can arrive from multiple locations. Data lakes, on the other hand, store and centralize data that comes in without processing it. Thus, there is no need to identify a specific purpose for the data as with a data warehouse environment. Your data, whether in its original form or curated form, can be stored in a data lake. Companies often choose a data lake for their flexibility in supporting any type of data, their scalability, analytics, machine learning capabilities, and low costs.

While Data Warehouses are appealing for their element of automatically curated data and fast results, data lakes can lead to several areas of improvement for your data and business including:

  • Improved customer interactions
  • Improved R&D innovation choices
  • Increase operational efficiencies

Essentially, a piece of information stored in a data lake will seem like a small drop in a big lake. Due to the lack of organization and security that tends to occur when storing large quantities of data in data lakes, this storing method has received some criticism. Additionally, setting up a data lake can be time and labor intensive, often taking months to complete. This is because, when built the traditional way, there are a series of steps that need to be completed and then repeated for different data sets.

Even once fully architected, there can be errors in the setup due to your data lakes being manually configured over an extended period. An important piece to your data lake is a data catalog, which uses machine learning capabilities to recognize data and create a universal schema when new datasets come into your data lake. Without defined mechanisms and proper governance, your data lake can quickly become a “data swamp”, where your data becomes hard to manage, analyze, and ultimately becomes unusable. Fortunately, there is a solution to all these problems. You can build a well-architected data lake in a short amount of time with AWS Lake Formation.

AWS Lake Formation & its Benefits

Traditionally, data lakes were set up as on-premises deployments before people realized the value and security provided by the cloud. These on-premises environments required continual adjustments for things like optimization and capacity planning—which is now easier due to cloud services like AWS Lake Formation. Deploying data lakes in the cloud provides scalability, availability, security, and faster time to build and deploy your data lake.

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days, saving your business a lot of time and effort to focus on other aspects of your business. While AWS Lake Formation significantly cuts down the time it takes to setup your data lake, it is built and deployed securely. Additionally, AWS Lake Formation enables you to break down data silos and combine a variety of analytics to gain data insights and ultimately guide better business decisions. The benefits delivered by this AWS service are:

  • Build data lakes quickly: To build a data lake in Lake Formation, you simply need to import data from databases already in AWS, other AWS sources, or from other external sources. Data stored in Amazon S3, for example, can be moved into your data lake, where your crawl, catalog, and prepare your data for analytics. Lake Formation also helps transform data with AWS Glue to prepare for it for quality analytics. Additionally, with AWS’s FindMatches, data can be cleaned and deduplicated to simplify your data.
  • Simplify security management: Security management is simpler with Lake Formation because it provides automatic server-side encryption, providing a secure foundation for your data. Security settings and access controls can also be configured to ensure high-level security. Ones configured with rules, Lake formation enforces your access controls. With Lake Formation, your security and governance standards will be met.
  • Provide self-service access to data: With large amounts of data in your data lake, finding the data you need for a specific purpose can be difficult. Through Lake Formation, your users can search for relevant data using custom fields such as name, contents, and sensitivity to make discovering data easier. Lake Formation can also be paired with AWS analytics services, such as Amazon Athena, Amazon Redshift, and Amazon EMR. For example, queries can be run through Amazon Athena using data that is registered with Lake Formation.

Building a data lake is one hurdle but building a well-architected and secure data lake is another. With Lake Formation, building and managing data lakes is much easier. On a secure cloud environment, your data will be safe and easy to access.

2nd Watch has been recognized as a Premier Consulting Partner by AWS for nearly a decade and our engineers are 100% certified on AWS. Contact us to learn more about AWS Lake Formation or to get assistance building your data lake.

-Tessa Foley, Marketing

3 Ways McDonald’s France is Preparing their Data for the Future

Data access is one of the biggest influences on business intelligence, innovation, and strategy to come out of digital modernization. Now that so much data is available, the competitive edge for any business is derived from understanding and applying it meaningfully. McDonald’s France is gaining business-changing insights after migrating to a data lake, but it’s not just fast food that can benefit. Regardless of your industry, gaining visibility into and governance around your data is the first step for what’s next.

1. No More Manual Legacy Tools

Businesses continuing to rely on spreadsheets and legacy tools that require manual processes are putting in a lot more than they’re getting out. Not only are these outdated methods long, tedious, subject to human error, and expensive in both time and resources – but there’s a high probability the information is incomplete or inaccurate. Data-based decision making is powerful, however, without a data platform, a strong strategy, automation, and governance, you can’t easily or confidently implement takeaways.

Business analysts at McDonald’s France historically relied on Excel-based modeling to understand their data. Since partnering with 2nd Watch, they’ve been able to take advantage of big data analytics by leveraging a data lake and data platform. Architected from data strategy and ingestion, to management and pipeline integration, the platform provides business intelligence, data science, and self-service analytics. Now, McDonald’s France can rely on their data with certainty.

2. Granular Insights Become Opportunities for Smart Optimization

Once intuitive solutions for understanding your data are implemented, you gain finite visibility into your business. Since completing the transition from data warehouse to data lake, McDonald’s France has new means to integrate and analyze data at the transaction level. Aggregate information from locations worldwide provides McDonald’s with actionable takeaways.

For instance, after establishing the McDonald’s France data lake, one of the organization’s initial projects focused on speed of service and order fulfilment. Speed of service encompasses both food preparation time and time spent talking to customers in restaurants, drive-thrus, and on the online application. Order fulfilment is the time it takes to serve a customer – from when the order is placed to when it’s delivered. With transaction-level purchase data available, business analysts can deliver specific insights into each contributing factor of both processes. Maybe prep time is taking too long because restaurants need updated equipment, or the online app is confusing and user experience needs improvement. Perhaps the menu isn’t displayed intuitively and it’s adding unnecessary time to speed of service.

Multiple optimization points provide more opportunity to test improvements, scale successes, apply widespread change, fail fast, and move ahead quickly and cost-effectively. Organizations that make use of data modernization can evolve with agility to changing customer behaviors, preferences, and trends. Understanding these elements empowers businesses to deliver a positive overall experience throughout their customer journey – thereby impacting brand loyalty and overall profit potential.

3. Machine Learning, Artificial Intelligence, and Data Science

Clean data is absolutely essential for utilizing machine learning (ML), artificial intelligence (AI), and data science to conserve resources, lower costs, enable customers and users, and increase profits. Leveraging data for computers to make human-like decisions is no longer a thing of the future, but of the present. In fact, 78% of companies have already deployed ML, and 90% of them have made more money as a result.

McDonald’s France identifies opportunity as the most important outcome of migrating to a data lake and strategizing on a data platform. Now that a wealth of data is not only accessible, but organized and informative, McDonald’s looks forward to ML implementation in the foreseeable future. Unobstructed data visibility allows organizations in any industry to predict the next best product, execute on new best practices ahead of the competition, tailor customer experience, speed up services and returns, and on, and on. We may not know the boundaries of AI, but the possibilities are growing exponentially.

Now it’s Time to Start Preparing Your Data

Organizations worldwide are revolutionizing their customer experience based on data they already collect. Now is the time to look at your data and use it to reach new goals. 2nd Watch Data and Analytics Services uses a five-step process to build a modern data management platform with strategy to ingest all your business data and manage the data in the best fit database. Contact Us to take the next step in preparing your data for the future.

-Ian Willoughby, Chief Architect and Vice President

Listen to the McDonald’s team talk about this project on the 2nd Watch Cloud Crunch podcast.

McDonald’s France Gains Business-Changing Insights from New Data Lake

McDonald’s is famous for cheeseburgers and fries, but with 1.5 million customers a day, and each transaction producing 20 to 30 data points, it has also become a technology organization. With the overarching goal to improve customer experience, and as a byproduct increase conversion and brand loyalty, McDonald’s France partnered with 2nd Watch to build a data lake on AWS.

Customer Priorities Require Industry Shifts

As is common in many industries today, the fast-food industry has shifted from a transaction centric view to a customer centric view. The emphasis is no longer on customer satisfaction, but on customer experience. It’s this variable that impacts conversion rate and instills loyalty. Consequently, McDonald’s wanted to build a complete perspective of a customer’s lifetime value, with visibility into each step of their journey. Understanding likes and dislikes based on data would give McDonald’s the opportunity to improve experience at a variety of intersections across global locations.

McDonald’s is a behemoth in its size, multi-national reach, and the abundance of data it collects. Making sense of that data required a new way of storing and manipulating it, with flexibility and scalability. The technology necessary to accomplish McDonald’s data goals has significantly reduced in cost, while increasing in efficiency – key catalysts for initiating the project within McDonald’s groups, gaining buy-in from key stakeholders, and engaging quickly.

From Datacenter to Data Lake

To meet its data collection and analysis needs, McDonald’s France needed a fault-tolerant data platform equipped with data processing architecture and a loosely coupled distribution system. But, the McDonald’s team needed to focus on data insights rather than data infrastructure, so they partnered with 2nd Watch to move from a traditional data warehouse to a data lake, allowing them to reduce the effort required to analyze or process data sets for different properties and applications.

During the process, McDonald’s emphasized the importance of ongoing data collection from anywhere and everywhere across their many data sources. From revenue numbers and operational statistics to social media streams, kitchen management systems, commercial, regional, and structural data – they wanted everything stored for potential future use. Historical data will help to establish benchmarks, forecast sales projections, and understand customer behavior over time.

The Data Challenges We Expect…And More

With so much data available, and the goal of improving customer experience as motivation, McDonald’s France wanted to prioritize three types of data – sales, speed of service, and customer experience. Targeting specific sets of data helps to reduce the data inconsistencies every organization faces in a data project. While collecting, aggregating, and cleaning data is a huge feat on its own, McDonald’s France also had to navigate a high level of complexity.

As an omnichannel restaurant, McDonald’s juggles information from point of sales systems with sales happening online, offline, and across dozens of different locations. Data sources include multiple data vendors, mobile apps, loyalty programs, customer relationship management (CRM) tools, and other digital interfaces. Combined in one digital ecosystem, this data is the force that drives the entire customer journey. Once it’s all there, the challenge is to find the link for any given customer that transforms the puzzle into a holistic picture.

Endless Opportunities for the Future

McDonald’s France now has visibility into speed of service with a dedicated dashboard and can analyze and provide syntheses of that data. National teams can make data-based, accurate decisions using the dashboard and implement logistical changes in operations. They’re able to impact operational efficiency using knowledge around prep time to influence fulfilment.

The data lake was successful in showing the organization where it was losing opportunities by not taking advantage of the data it had. McDonald’s also proved it was possible, affordable, and advantageous to invest in data. While their data journey has only begun, these initial steps opened the door to new data usage possibilities. The models established by McDonald’s France will be used as an example to expand data investments throughout the McDonald’s corporation.

If your organization is facing a similar of issue of too much data and not enough insight, 2nd Watch can help. Our data and analytics solutions help businesses make better decisions, faster, with a modern data stack in the cloud. Contact Us to start talking about the tools and strategies necessary to reach your goals.

-Ian Willoughby, Chief Architect and Vice President

Listen to the McDonald’s team talk about this project on the 2nd Watch Cloud Crunch podcast.

Cloud Crunch Podcast: Examining the Cloud Center of Excellence

What is a Cloud Center of Excellence (CCOE), and how can you ensure its success? Joe Kinsella, CTO of CloudHealth, talks with us today about the importance of a CCOE, the steps to cloud maturity, and how to move through the cloud maturity journey. We’d love to hear from you! Email us at CloudCrunch@2ndwatch.com with comments, questions and ideas. Listen now on Spotify, iTunes, iHeart Radio, Stitcher, or wherever you get your podcasts.

Cloud Crunch Podcast: Diving into Data Lakes and Data Platforms

Data Engineering and Analytics expert, Rob Whelan, joins us today to dive into all things data lakes and data platforms. Data is the key to unlocking the path to better business decisions. What do you need data for? We look at the top 5 problems customers have with their data, how the cloud has helped solve these challenges, and how you can leverage the cloud for your data use. We’d love to hear from you! Email us at CloudCrunch@2ndwatch.com with comments, questions and ideas. Listen now on Spotify, iTunes, iHeart Radio, Stitcher, or wherever you get your podcasts.