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What Is a Data Pipeline?

You may have seen the iconic episode of "I Love Lucy" where Lucy and Ethel get jobs wrapping chocolates in a candy factory. The high-speed conveyor belt starts up and the ladies are immediately out of their depth. By the end of the scene, they are stuffing their hats, pockets, and mouths full of chocolates, while an ever-lengthening procession of unwrapped confections continues to escape their station. It's hilarious. It's also the perfect analog for understanding the significance of the modern data pipeline.

The efficient flow of data from one location to the other - from a SaaS application to a data warehouse, for example - is one of the most critical operations in today's data-driven enterprise. After all, useful analysis cannot begin until the data becomes available. Data flow can be precarious, because there are so many things that can go wrong during the transportation from one system to another: data can become corrupted, it can hit bottlenecks (causing latency), or data sources may conflict and/or generate duplicates. As the complexity of the requirements grows and the number of data sources multiplies, these problems increase in scale and impact.

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Database Migration Tools

There's no denying that the world is driven by data. And that data usually lives in a database. As enterprises like yours increasingly look to extract maximum value and insights from data through big data analytics, they're finding that sometimes it's necessary to move their data from one database to another. This process is called, appropriately, database migration.

Database migration tools allow you to literally move data from one type of database to another or to another destination like a data warehouse or data lake. Migrating databases - say, from on-premise to the cloud - can help reduce costs, improve business agility with more flexible systems, and centralize enterprise data to create a single source of truth.

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Top Cloud Data Security Challenges

Almost three-quarters of businesses will run nearly their entire operations on the cloud by 2020. Organizations are flocking en masse to cloud computing, eager to capitalize on the speed, scale, and flexibility a cloud-based infrastructure can provide. But as cloud computing grows in popularity and transforms how companies collect, use, and share data, it also becomes a more attractive target for would-be attackers and hackers.

Cloud providers have invested time and resources into bolstering cloud security and boosting customer confidence. Solutions that were once believed to be fraught with risk have been strengthened through containerization, encryption, advanced failover, and automated threat detection capabilities.

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What Is Data Integrity?

Data Integrity Explained

Data integrity is the assurance of accuracy and consistency of data over the course of the data life cycle (from when the data is recorded until it is destroyed). In simple terms, data integrity means that you have recorded the data as intended and that it wasn't unintentionally changed over the course of its life cycle. The concept is simple, but the practice is not. Data integrity is a critical component to creating or designing any software system that will store or move data.

Benefits

Data integrity is important because just about every critical business decision is based on a company's data. With good data integrity, you can analyze your company's data to answer questions like: what were your business achievements? What were your business expenses? How are your sales in different regions? Are there areas of your business where expenses are growing faster than income? What is the productivity of different divisions of your workforce? Are you meeting your benchmark goals? Can you forecast your expenses for the upcoming fiscal year? If you don't have good data, you can't answer any of these questions accurately.

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What Is Data Redundancy?

Data Redundancy Explained

Data redundancy occurs when the same piece of data is stored in two or more separate places. Suppose you create a database to store sales records, and in the records for each sale, you enter the customer address. Yet, you have multiple sales to the same customer so the same address is entered multiple times. The address that is repeatedly entered is redundant data.

How Does Data Redundancy Occur?

Data redundancy can be designed; for example, suppose you want to back up your company’s data nightly. This creates a redundancy. Data redundancy can also occur by mistake. For example, the database designer who created a system with a new record for each sale may not have realized that his design caused the same address to be entered repeatedly. You may also end up with redundant data when you store the same information in multiple systems. For instance, suppose you store the same basic employee information in Human Resources records and in records maintained for your local site office.

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What Is Data Mining?

Everyone wants an edge. And in the digital age of business, the greatest strategic advantage comes from slicing, dicing, and analyzing data from every possible angle.

Data mining is the automated process of sorting through huge data sets to identify trends and patterns and establish relationships. And as enterprise data proliferates — now over 2.5 quintillion bytes per day — it'll continue to play an increasingly important role in the way businesses plan their operations and address challenges in the future.

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What Is Data Validation?

Data validation is a method for checking the accuracy and quality of your data, typically performed prior to importing and processing. It can also be considered a form of data cleansing. Data validation ensures that your data is complete (no blank or null values), unique (contains distinct values that are not duplicated), and the range of values is consistent with what you expect. Often, data validation is used as a part of processes such as ETL (Extract, Transform, and Load) where you move data from a source database to a target data warehouse so that you can join it with other data for analysis. Data validation helps ensure that when you perform analysis, your results are accurate.

Steps to Data Validation

Step 1: Determine Data Sample

Determine the data to sample. If you have a large volume of data, you will probably want to validate a sample of your data rather than the entire set. You’ll need to decide what volume of data to sample, and what error rate is acceptable to ensure the success of your project.

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Data Lake vs. Data Warehouse

Data lakes and data warehouses are critical technologies for business analysis, but the differences between the two can be confusing. How are they different? Is one more stable than the other? Which one is going to help your business the most? This article seeks to demystify these two systems for handling your data.

What Is a Data Lake?

A data lake is a centralized repository designed to store all your structured and unstructured data. Further, a data lake can store any type of data using its native format, without size limits. Data lakes were developed primarily to handle the volumes of big data, and thus they excel at handling unstructured data. You typically move all the data into a data lake without transforming it. Each data element in a lake is assigned a unique identifier, and it is extensively tagged so that you can later find the element via a query. The benefit of this is that you never lose data, it can be available for extensive periods of time, and your data is very flexible because it does not need to adhere to a particular schema before it is stored.

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What Is Data Profiling?

Data profiling is a process of examining data from an existing source and summarizing information about that data. You profile data to determine the accuracy, completeness, and validity of your data. Data profiling can be done for many reasons, but it is most commonly part of helping to determine data quality as a component of a larger project. Commonly, data profiling is combined with an ETL (Extract, Transform, and Load) process to move data from one system to another. When done properly, ETL and data profiling can be combined to cleanse, enrich, and move quality data to a target location.

For example, you might want to perform data profiling when migrating from a legacy system to a new system. Data profiling can help identify data quality issues that need to be handled in the code when you move data into your new system. Or, you might want to perform data profiling as you move data to a data warehouse for business analytics. Often when data is moved to a data warehouse, ETL tools are used to move the data. Data profiling can be helpful in identifying what data quality issues must be fixed in the source, and what data quality issues can be fixed during the ETL process.

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What Are Data Silos?

A data silo is a collection of information in an organization that is isolated from and not accessible by other parts of the organization. Removing data silos can help you get the right information at the right time so you can make good decisions. And, you can save money by reducing storage costs for duplicate information.

How Do Data Silos Occur?

Data silos happen for three common reasons:

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What Is Data Loading?

One of the most important aspects of data analytics is that data is collected and made accessible to the user. Depending on which data loading method you choose, you can significantly speed up time to insights and improve overall data accuracy, especially as it comes from more sources and in different formats. ETL (Extract, Transform, Load) is an efficient and effective way of gathering data from across an organization and preparing it for analysis.

Data Loading Defined

Data loading refers to the "load" component of ETL. After data is retrieved and combined from multiple sources (extracted), cleaned and formatted (transformed), it is then loaded into a storage system, such as a cloud data warehouse.

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What Is Data Consolidation?

To the outside world, your business is a highly organized structure. But on the inside, it's a cauldron of raw material collected from databases, documents, and a multitude of other sources. This material - a.k.a. data - has all the potential in the world to help your business transform and grow, so long as you properly corral it all through a process called data consolidation.

Data Consolidation Defined

Data is generated from many disparate sources and in many different formats. Data consolidation is the process that combines all of that data wherever it may live, removes any redundancies, and cleans up any errors before it gets stored in one location, like a data warehouse or data lake.