TidyData offers data pipeline monitoring for companies that want to make sure their data is accurate. With our services, you can see holes in your pipeline and make corrections.
What is a Data Pipeline?
A data pipeline is a software that defines where the data is collected from and what data is collected. Typically the data is sent to a data warehouse for storage and processing later. A good data pipeline automates extracting, transforming, combining, validating, transferring the data, and also checking for errors and maintaining security. Let’s take a closer look at these terms below.
This is simply the process of taking the data from the source. In today’s world data is gathered from many different sources in order to see the big picture. To simplify an example: an online store would want data from their sales as well as where their traffic is coming from.
Once the data is collected from the source it needs to be transformed so the data warehouse can read the data. The data may be collected as CSV files, XML, or simply raw data but most databases will use SQL to read and store the data.
Combining the data is how the data pipeline packages the data to send to the data warehouse. Once the data has been transformed to something the data warehouse can read, it needs to be combined in a way that the data warehouse can read. An easy way to understand this concept is to think about sheets of paper. We can read sheets of paper, they are a format we can understand, but if we were to send thousands of individual sheets we may not know what to do with them. If we instead combine them into books the sets of “data” make much more sense.
Data verification is ensuring that the data entered is what is expected. If someone enters a name into a date field it wouldn’t make much sense to our database. Fortunately there are ways to easily prevent this on the user’s end but if these are bypassed or missed we still want to make sure the data we receive matches what is expected.
This is the actual sending of the data from one location to another. This is an important step to mention because there are many things that can go wrong during this process. Network inconsistencies can interfere with the data transfer and cause errors or incomplete data transfers. Making sure there is enough bandwidth and processes in place to ensure error checking can reduce the majority of these issues.Error Checking
Error checking the data is the process of making sure that all the data was transferred, made it to its destination and was not corrupted in the process. Error checking can be done with checksums or reviewing the metadata of the packages sent.Security
Ensuring that the data isn’t intercepted is important to many organizations who want to keep their trade secrets and other intellectual property.
Why is a Data Pipeline Important?
Data Pipeline management is important for many reasons including the terms listed above. Managing a data pipeline can be a daunting task for a company because there are many different aspects to incorporate.
What is Data Pipeline Architecture?
A data pipeline architecture is the way in which the data pipeline is set up. The architecture behind the data pipeline should be simple, but effective. In order to do this, you must define where the data is collected, transformed, and loaded.
How Data Pipelines Work?
Data pipelines work by collecting data and telling it where to go. A good data pipeline architecture will also verify the data and ensure security of the information along the way. Where the information is collected from as well as what data will be collected must be specified. Below are the different parts to a general data pipeline architecture.
Data will be collected from a variety of different sources such as relational databases, Application APIs, marketing tools, and other sources. The schema behind the data, as well as the data itself, must first be analyzed to create an effective data pipeline architecture.
A join is a database term but it can be described simply as the logic behind how the data is combined. When dealing with big data, there is often overlap of information. By using joins we can filter out the unnecessary or redundant information and store only what we want to see and use.
With big data there is often tiny data within other data sets. For instance an address field will often contain a zip code or a phone number, an area code. These subsets of data can often be difficult to query if not set up correctly.
Data normalization is the process of standardizing the data. A great real-world example of this would be looking at clothes or shoe sizes. Each country tends to have their own industry standard but in order to store this in a database, the sizes, or in our case units of data, must meet the standard we expect.
This is the process of correcting mistakes or errors in the data. These can come from inconsistencies in field input and also corrupt records from where the data is being mined. By correcting the data ahead of time, we can reduce the time it takes to transform and analyze the data later.
Loading the data is a process that depends on where the data is being sent. Each service may have it’s own requirements even though they are typically relational databases (RDBMS).
Data pipelines are often performed on a schedule or regular basis which can be automated easily. Other areas of automating the process can include error checking and also monitoring reports. Customers will often want to know what the progress or status of the data pipelines are.
How is a Data Pipeline used for Analytics?
Analytics is the systematic process of automatically computing the data. That’s nice to define, but what do we use analytics for? Customers want to see analytics that answer questions. Where is the data coming from? How much data is coming from there? Where the spikes in data? What is the latency between data mining and representation? All these questions and more can be answered by a good data pipeline management system that queries these statistics for analysis.
Data Pipeline and Workflow Management Tools??
Workflow management tools capture and create a lot of the data that a data pipeline deals with. The data pipeline management system will transfer the data from the workflow management tool and send it to the data warehouse.
Online Transaction Processes are workflow management tools, sales services, or financial transactions. These typically store data for only a short period of time and tend to have limited analytics.
Online Analytical Processing are typically your data warehouse or data lakes. These software report to analytical queries from multiple sources and are specialized for dealing with large amounts of queries.
What is ETL?
ETL stands for Extract, Transfer, Load. This is the process of taking the data from one endpoint to the other using the same service. ETL tends to use OLAP warehouses with SQL databases. The data is gathered from the sources, transformed into useful data for the database, and loaded into it’s final destination.
Data Pipeline vs ETL?
The difference between a data pipeline and an ETL is that with an ETL everything is locked in place with an ETL. With a data pipeline, you can easily change the data sources or the destination of the data. The pipeline simply manages the data across the transfer route to make sure it safely and securely reaches its destination.
Data warehouse monitoring is exactly what it sounds like but there is more than meets the eye. Data warehouse monitoring software involves the tracking of: Data Loads Queries and Reports (reports are typically sets of queries), Archiving data, Backups and data restoration, Data warehouse monitoring activities can be better explained when we look at the subsets of activities.
Monitoring data usage is a critical part of effectively managing a data warehouse because if we know the high usage data, we can make that data more readily available. There may be some data which is only queried once or twice a year. This may be able to be removed and made available only when necessary. For high usage queries, an aggregated table may be used to have the data pre-loaded for quicker access.
Warehouse user monitoring is important because it identifies which users are heavy users, and which users may no longer need access. This helps the data warehouse monitoring team offer additional support.
Data response time monitoring can help the data warehouse team identify problems that may be occuring within the data warehouse. A long response from a particular table may mean corrupt data or simply that the table needs to be optimized to better fit the customer’s needs.
By monitoring the data warehouse activities, the data warehouse team can identify things like large queries and when they take place, or if data loads are occuring during peak usage hours causing lag times in response.
What is Tidy Data?
Tidy data, or small data, is data that is created as a subset of the BIg Data. For example, you might be able to figure out how much downtime employees have between customers or the percentage of your customers that are actually sticking on the line to fill out that end of call survey. Tiny data is useful to companies because it can help improve processes to better the customer experience or improve efficiency in other areas. Big data gives the overall picture while tiny data can really delve down into the details of where problems may lie or where experiences can be improved.
How Can TidyData Help You??
TidyData can help your company by correctly monitoring your data pipelines. We know how important data is, big data and small data, and that you want to keep your data safe and secure. At TidyData we specialize in correctly monitoring your data pipelines so you can focus on the things that can make your company great. Instead of worrying about how to create your own data pipeline architecture, let us do it for you and free up your developers for those other projects to improve your customer’s experience.
Contact TidyData Today For More?
Contact TidyData today to set up your secure data pipeline monitoring system with us. We offer a SaaS for your company to free up space on your systems and free up your developer’s time so they don’t need to take on the laborious task of data pipeline management. Visit www.tidydata.io to set up an appointment for us to assess your data solution needs. At TidyData we work with our customers to create the best data management solution to fit their needs and are always looking to improve the data pipeline management system for the both of us.