Data lakes have grown to be the keystone in several big data initiatives, just because they provide easier and more flexible alternatives when working with high volumes of data. This huge amount of data is generated at a high velocity, such as data from web, sensor, or app activities. Since such types of data sources have become even more common, interest in data lakes services has also been rising at a rapid rate.
However, just like any other emerging technology, there is no one-size-fits-all: a data lake might be an outstanding fit for some, but in other scenarios, going for tested database architectures will be the better option. Below are four indications that will help you understand whether you are prepared to join the data lake or if you should keep using traditional data warehousing.
The data lake is a method defined by big data architecture that concentrates on storing unstructured or semi-structured data in its original format, in a single repository that works for numerous analytic use cases or services. Storage and compute resources are separated so that data at rest exists on economical object storage, such as Hadoop on-premise or Amazon S3- while a variety of tools and services like Apache Presto, Elasticsearch, and Amazon Athena can be utilized for querying that data.
It is different from traditional database or data warehouse architectures, where compute and storage are combined, and the data is structured, leading ingestion to implement an established schema. Data lakes make it simpler to approve a store now, evaluate later methods, as there is very slight effort included in ingesting data into the lake. However, when it is a matter of evaluating the data, some of the traditional data preparation result in challenges.
How would you know that your organization really needs a data lake? Let’s start by looking at these four key indicators.
Data lakes are exceptional for storing huge volumes of unstructured and semi-structured data. Storing such types of data in a database will need broad data preparation, as databases are constructed around structured tables rather than raw events that would exist in JSON / XML format.
If the majority of your data is made up of structured tables; for example, preprocessed CRM documents or financial balance sheets, then it could be easier to keep the same database. However, if you’re working with a big amount of event-based data, such as server logs or clickstreams, it can be easier to store that data in its raw form and make specific ETL flows on the basis of your use case.
ETL (extract-transform-load) is a process that essentially puts your data to use. However, when functioning with big or streaming data, it can develop into a major barrier due to the complexity of writing ETL tasks using code-demanding frameworks like Spark/Hadoop.
To reduce the number of resources you are investing in ETL, try to recognize where the major blockage occurs. If you’re frequently struggling with frustrating try and fit semi-structured and unstructured data into your relational database, it might be time to consider making the transition to a data lake. However, you might still experience a lot of challenges in generating ETL flows from the lake to the variety of target services you’ll make use of for analytics, machine learning, etc. – in which case you might consider using a data lake ETL tool to automate several of these processes.
As you know, databases combine storage with computing, so storing huge volumes of data in a database gets costly. It leads to many fidgeting with data retention, either trimming some definite fields off the data or limiting the period in which you store to historical data for controlling expenses.
If your organization is continuously under strain to gain the right balance between storing the data for analytical reasons versus disposing of data to control costs, a data lake solution might be right for you. Because Data Lake architectures are generated as low-cost object storage, it allows you to store terabytes or even petabytes of historical data without putting a deep hole in your pocket.
The last question you should ask is what you plan to do with the data. If you’re only trying to create a report or set of reports, or dashboards which will basically be produced by running a fixed set of queries against tables that are repeatedly updated, a data warehouse will most likely be a brilliant solution, as you can simply establish such processes using SQL and existing data warehouse and business intelligence tools.
However, for additional experimental use cases, such as machine learning and predictive analytics, it is harder to understand in advance what data you will require and how you want to query it. In these scenarios, data warehouses can be extremely ineffective as the predefined plan will limit your capacity to discover the data. On these occasions, a data lake could be a better choice.
When your data achieves a definite level of size and complexity, data lakes are absolutely the way to go. Has your organization reached that point yet? You need to get answers to these four questions detailed above to try and find out.
Moreover, if you want to provide high-quality, actionable data to your business rapidly, you require an automated lake house and data management solution that allows you to get a complete view of where all your significant data resides across various silos, applications and regions.
Analysts can now start with a data lake to experiment assumptions on huge volumes of data, then extract and load the most functional data into a warehouse for decision-making. Exactly like a real lake, data lakes can be cluttered below the surface, but they can delight and inform when the resources prowling there are exposed.
If you don’t have experienced IT staff or a specialized IT team, you would be wise to hire professional Data Lake Services. ExistBI have experienced consultants in the United States, United Kingdom and Europe, contact us today for more support and information.
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