A data lake is an absolutely vital piece of today’s big data business environment. A single company may have incoming data from a huge variety of sources, and having a means to handle all of that is essential. For example, your business might be compiling data from places as diverse as your social media feed, your app’s metrics, your internal HR tracking, your website analytics, and your marketing campaigns. A data lake can help you get your arms around all of that, funneling those sources into a single consolidated repository of raw data.
But what can you do with that data once it's all been brought into a data lake? The truth is that putting everything into a large repository is only part of the equation. While it’s possible to pull data from there for further analysis, a data lake without any integrated tools remains functional but cumbersome, even clunky.
On the other hand, when a data lake integrates with the right tools, the entire user experience opens up. The result is streamlined access to data while minimizing errors during export and ingestion. In fact, integrated tools do more than just make things faster and easier. By expediting automation, the door opens to exciting new insights, allowing for new perspectives and new discoveries that can maximize the potential of your business.
To get there, you’ll need to put the right pieces in place. Here are four essential tools to integrate into your data lake experience.
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Even if your data sources are vetted, secured, and organized, the sheer volume of data makes it unruly. As a data lake tends to be a repository for raw data—which includes unstructured items such as MP3 files, video files, and emails, in addition to structured items such as form data—much of the incoming data across various sources can only be natively organized so far. While it can be easy to set up a known data source for, say, form data into a repository dedicated to the fields related to that format, other data (such as images) arrives with limited discoverability.
Machine learning can help accelerate the processing of this data. With machine learning, data is organized and made more accessible through various processes, including:
In processed datasets, machine learning can use historical data and results to identify patterns and insights ahead of time, flagging them for further examination and analysis.
With raw data, machine learning can analyze usage patterns and historical metadata assignments to begin implementing metadata automatically for faster discovery.
The latter point requires the use of a data catalog tool, which leads us to the next point.
Simply put, a data catalog is a tool that integrates into any data repository for metadata management and assignment. Products like Oracle Cloud Infrastructure Data Catalog are a critical element of data processing. With a data catalog, raw data can be assigned technical, operational, and business metadata. These are defined as:
By implementing metadata, raw data can be made much more accessible. This accelerates organization, preparation, and discoverability for all users without any need to dig into the technical details of raw data within the data lake.
A data lake acts as a middleman between data sources and tools, storing the data until it is called for by data scientists and business users. When analytics and other tools exist separate from the data lake, that adds further steps for additional preparation and formatting, exporting to CSV or other standardized formats, and then importing into the analytics platform. Sometimes, this also includes additional configuration once inside the analytics platform for usability. The cumulative effect of all these steps creates a drag on the overall analysis process, and while having all the data within the data lake is certainly a help, this lack of connectivity creates significant hurdles within a workflow.
Thus, the ideal way to allow all users within an organization to swiftly access data is to use analytics tools that seamlessly integrate with your data lake. Doing so removes unnecessary manual steps for data preparation and ingestion. This really comes into play when experimenting with variability in datasets; rather than having to pull a new dataset every time you experiment with different variables, integrated tools allow this to be done in real time (or near-real time). Not only does this make things easier, this flexibility opens the door to new levels of insight as it allows for previously unavailable experimentation.
In recent years, data analysts have started to take advantage of graph analytics—that is, a newer form of data analysis that creates insights based on relationships between data points. For those new to the concept, graph analytics considers individual data points similar to dots in a bubble—each data point is a dot, and graph analytics allows you to examine the relationship between data by identifying volume of related connections, proximity, strength of connection, and other factors.
This is a powerful tool that can be used for new types of analysis in datasets with the need to examine relationships between data points. Graph analytics often works with a graph database itself or through a separate graph analytics tool. As with traditional analytics, any sort of extra data exporting/ingesting can slow down the process or create data inaccuracies depending on the level of manual involvement. To get the most out of your data lake, integrating cutting-edge tools such as graph analytics means giving data scientists the means to produce insights as they see fit.
Oracle Big Data Service is a powerful Hadoop-based data lake solution that delivers all of the needs and capabilities required in a big data world: