Data lakes were formed as a solution to storing unstructured data – an alternative to the restrictive nature of data warehouses. But this ease comes with its own set of unique challenges that organizations are finding hard to overcome.
In fact, some data lake challenges are so hard to overcome that organizations are abandoning the idea of maintaining a data lake.
Leading analyst firms have been quoting data lake failure rates of 85% for some time now. (Teradata)
What are these unique challenges and is there a way for organizations to maintain their data lake and meet the purpose for which it was designed?
Let’s discover more.
Why Data Lakes and Not Data Warehouses?
An enterprise-level organization is connected to at least 464 applications. The amount of information streaming in from all those applications needs to be stored somewhere. We’re talking about all kinds of structured and semi-structured data that is collected through multiple data sources; mobile apps, web apps, activity logs, phone logs, social media and hundreds of other sources.
All this data combined makes up for business intelligence that organizations need to make strategic business decisions.
Data warehouses, which were the traditional methods of storing enterprise data requires data to be structured. You could not dump data into a data warehouse without sorting or aligning it to the defined structure.
Data lakes overcame this limitation. With the implementation of data pipelines, all data sources could be transported to the lake and kept there until the company needed data for analytics, reporting, and BI.
While data lakes solved the problem of holding data, it posed a significant challenge – that of data quality.
Because data is simply dumped into the system, there seems to be no way for analysts to determine data quality. An initial checkup was not performed. Moreover, the biggest challenge in light of recent regulations is data privacy and data compliance. With no one to account for the quality of data, organizations are lost when dealing with raw data.
Enters data ingestion.
How Does Data Ingestion Help with Data Lake Challenges?
Data ingestion is the layer between data sources and the data lake itself. This layer was introduced to access raw data from data sources, optimize it and then ingest it into the data lake.
Yet, it’s surprising to see that data ingestion is used as an after-thought or after data is inserted into the lake. In fact, most organizations completely miss the data ingestion process as they underestimate the complexity of moving data from data sources into the data lake. It is only at a critical moment in time when they need data do they realize that they have a significant challenge at hand.
If you see to it, the whole purpose of having a data lake is to store data that can be used for later without worrying about its structure – but that doesn’t literally mean to have it ingested in the system without cleaning it or making sure it adds value.
If data is not managed, the data lake becomes a data swamp, where you have muddled data sitting in a repository that cannot be used nor can be analyzed. This beats the purpose of a data lake, which thus is the leading cause for most data lake projects to fail.
Let us repeat:
Ingestion is a planned process and one that must be done separately before data is entered into the system. This planned process must follow the objective of having complete, accurate and consistent data over time.
Do note that data ingestion does not mean the perfecting of raw data. It simply allows for the maintenance of a basic organization where duplicates are removed, incomplete or null information is highlighted – making it easy for any data set to be available for immediate analysis.
Data Ingestion Functions
While most data lakes today incorporate data ingestion, key functions are often missed. Here are three important functions of ingestion that must be implemented for a data lake to have usable, valuable data.
- The Data Collection Process: Data ingestion’s primary purpose is to collect data from multiple sources in multiple formats – structured, unstructured, semi-structured or multi-structured, make it available in the form of stream or batches and move them into the data lake.
- The Filtration Process: At this early stage of a data lifecycle, the data is passed through a basic filtration and sanitization process where parsing and de-duplication activities are done. Other complex operations such as identifying and removing invalid or null data values can also be performed using scripts.
- The Transportation Process: Transporting the data into its respective stores within the data lake is a process that depends on the clarity of routing rules and the automation procedures that are set up.
Batch vs Streaming Ingestion
There are two kinds of ingestion models and both depend on the kind of requirements or expectations businesses will have from their data.
Batch Processing: This is the most common kind of data ingestion where groups of source data is periodically collected and sent to the destination system. There could be a simple schedule set in place where source data is grouped according to a logical ordering or certain conditions. Batch processing is generally easier to manage via automation and is also an affordable model.
Streaming: This is based on real-time processing that does not involve any grouping. Data is loaded as soon as it appears and is recognized by the ingestion layer. While this is an expensive and more complex model, it works effectively for organizations that require immediate, continuous, refreshed data.
Data Lake Ingestion Challenges
While data ingestion attempts to resolve data lake challenges, it is not without its own set of challenges. Certain difficulties can impact the ingestion layer, which in turn impacts the data lake performance.
Let’s take a look at some key challenges.
Managing the Volume of Incoming Data with Speed
Data volumes have exploded and as the global ecosystem becomes more connected and integrated, data volumes will rise exponentially. Moreover, data sources themselves are constantly evolving which means data lakes and data ingestion layers have to be robust enough to ingest this volume and diversity of data. The challenge is even more difficult to overcome when organizations implement a real-time data ingestion process that requires data to be updated and ingested at rapid speed.
Since data ingestion and data lakes are fairly new technologies, they are yet to reach breakneck speed. Depending on the application, real-time data processing could take up to 10 minutes for every update.
Meeting New Data Compliance Guidelines
Legal data compliance from countries around the globe has made it difficult for companies to sort their data according to regulatory compliances. Companies need to comply with Europe’s GDPR as well as with dozens of other compliance regulations in the US. Therefore, data needs to be sorted according to these regulations at the ingestion layer to prevent any problems down the line. This calls for holistic data ingestion planning.
Cleansing Data for Data Preparation
This is a highly overlooked challenge of data lakes. Somehow, it’s assumed that the cleansing process should only take place when data is required for analysis. Not only does this approach cause significant bottlenecks, but it also leaves the company exposed to the other two challenges of data privacy and data security above.
Cleansing data for data preparation ideally must begin before the data is ingested into the lake. Performing the basic sanitization will save the data team from wasting their time in trying to make sense of raw data. At this stage, raw data should be filtered for duplicates, incomplete and invalid fields etc. With this done, analysts may then choose to perform further tuning or optimization for their intended purpose.
Data Quality in Data Lake Ingestion
Whether it’s during the data ingestion phase or at the data transformation phase, a data quality solution will be required to process data before it is made use for analytics. When we talk about data quality, we’re primarily focusing on:
- Cleaning raw data of typos, structural issues such as spellings, lower and upper cases etc
- Invalid, incomplete, null or void fields
- Most importantly, duplicated data that becomes a major bottleneck down the line
To perform data cleansing you will need the implementation of a data quality tool one that can let you process raw data directly from your data source.
Data Ladder’s DataMatch Enterprise is a powerful tool that can be used to clean, match and dedupe raw data. It allows for integration to 150+ apps and databases which means you can use it as a tool to capture your data before it is moved into the data lake.
It can also be used at the data transformation phase if you want to delve deeper into the issues of your data such as data profiling, address verification and validation etc.
The tool is deployed as an on-premises solution which you can use on your desktop or cloud server. Plus point? This tool performs both batch and real-time processing, while also allowing you to schedule future processes.
The Bottom Line
It is significant to implement a proper ingestion infrastructure that allows data lake to store complete, well-timed and consumption-ready data. Unlike a data warehouse, data lakes excel at utilizing the availability of huge amounts of coherent data to improve real-time decision analytics. It is not only useful in advanced predictive analytical applications but can also be productive in reliable organizational reporting, particularly when it contains different data designs.
For data lakes to work though, data ingestion must be planned as a separate activity and data quality must be the primary objective. When data quality is ignored, it creates a ripple of problems that effects the entire pipeline – from data collection to the final product.
Want to learn more about how we can help you during the data ingestion process? Get in touch with us and let our solution architect guide you through the journey.
