Over the years, there has been a significant increase in the use of big data for better decision-making, IT infrastructure, and more efficient operations. And these data, which are either structured or unstructured needs to be processed quickly and correctly to identify information useful for business needs. There is need to munge every data produced by an organization to give more valuable insight in finer granularity, and this has given birth to the new term called “Data Lake” in the digital universe.
Big Data technologies are sometimes considered as destructive technologies as they revolutionized the traditional ways of doing things in this data-intensive era. Concepts from distributed and parallel system are reapplied as the foundation of big data such as MapReduce paradigms for handling the big Vs characteristics – volume, velocity, variety, value and value. The incumbent SQL databases with ACID characteristics are challenged (and sometimes even replaced) by NoSQL databases with BASE characteristics.
Now, Data Lake concept is trying to challenge the reliable, traditional data warehouses for storing heterogeneous complex data. It has become the new buzz word in the IT industry. Everyone is talking about it and repeatedly using it to impress others, even if they don't know what it means. It is often used out of context and more as a marketing gimmick. In this article, we will explore what Data Lake is and how it will be useful for everyday computing.
What Is A Data Lake?
Data Lake has got different definitions, and so, it is worth clarifying the definition used in this article. A data lake is known as a massively scalable storage repository that holds a vast amount of raw data in its native format until it is needed plus processing systems (engine) that can ingest data without compromising the data structure.
Importance of A Data Lake
The data lake incorporates an organization’s data into a controlled and well-managed environment that encourages both analytics growth and production workloads. It embraces various data platforms, such as relational data warehouses, Apache Hadoop clusters, and analytical appliances, and controls them together through a standard system.
Some of the benefits of Data Lakes include:
Improved data trust: Data Lakes allow organizations to make better, honest decisions with their data rather than rely on the false data presented.
Improved customer experience: A Data Lake can be built to combine customer data from different sources such as CRM platform, social media analytics, a marketing platform that includes buying history, incident tickets which enables a business to understand the most profitable customer, the promotions or rewards that will increase loyalty, etc.
Complexity reduction: Ovеr thе уеаrѕ the analytical lаndѕсаре might hаvе become соmрlеx wіth numerous dаtа wаrеhоuѕеѕ аnd data marts wіth complex ѕеtѕ оf interfaces. Tо provide agility аnd flеxіbіlіtу, these environments need tо bе aligned and mаdе mоrе consistent.
Increase operational efficiencies: The Internet оf Things (IoT) hаѕ іntrоduсеd more wауѕ tо collect data оn рrосеѕѕеѕ with rеаl-tіmе dаtа соmіng from different devices соnnесtеd tо thе internet. A dаtа lаkе mаkеѕ it еаѕу tо ѕtоrе, аnd run аnаlуtісѕ оn mасhіnе-gеnеrаtеd IоT dаtа tо discover wауѕ to reduce ореrаtіоnаl соѕtѕ, and іnсrеаѕе quality.
Data Lakes Vs. Data Warehouses
Generally, most organizations will need both a data warehouse and a data lake as they serve different unique purposes and it depends on the requirements.
While the data lakes are typically built to handle large and quickly arriving volumes of unstructured data, from which further insights are derived, data warehouses deals with highly structured data,. Thus, the data lakes use dynamic not pre-build static like in data warehouses analytical applications.
The data in the lake becomes accessible as soon as it is created, while that of data warehouses is designed for slowly changing data.
In contrast to a hierarchical data warehouse with files or folders data storage, the data lake uses a flat architecture, where each data element has a unique identifier and a set of extended metadata tags. The data lake does not require a rigid schema or manipulation of the data of all shapes and sizes, but it requires maintaining the order of the data arrival.
Data Lakes can be imagined as a large data pool to bring in all of the historical data accumulated and new data (structured, unstructured and semi-structured plus binary from sensors, devices and so on) in near real-time into one single place, in which the schema and data requirements are not defined until the data is queried.
Today, many organizations with data warehouses are already evolving their warehouse to include data lakes after having seen the various benefits of data lakes. This allows them to enable diverse query capabilities, data science use-cases, and advanced capabilities for discovering new information models.
The challenges of Data Lakes
One main challenge seen with the use of data lake is the inability of data to be stored without an oversight of its contents. For a data lake to make data usable, it needs to have defined mechanisms to catalogue, and secure data. Without these elements, data cannot be found, or trusted resulting in a “data swamp." Meeting the needs of wider audiences require data lakes to have governance, semantic consistency, and access controls.
Data Lake Platforms: Building your Data Lakes
Before selecting a data platform for a data lake, you need to determine the lake’s relational requirements and also understand the essential elements and capabilities of a Data Lake. These include Advanced analytics, New data-driven practices, ability to handle big data, ability to undergo the modernization process (Data lakes are regularly added to multiplatform data warehouse environments (DWEs) as part of the modernization process),high security, and machine learning.
You can ask your team the following questions to know which type of Data Lake (Hadoop or RDBMS) is right for you?
Is your team under tight cost restrictions?
Do we need advanced RDBMS data management functions, such as OLAP, materialized views, and complex data models (dimensional or hierarchical)?
Will the lake push the extremes of scalability?
Do we need mature RDBMS functions, such as metadata, indexing, security, volumes, and partitioning?
Does your team’s culture work well with open source?
Will the lake manage lots of file-based data?
Does your team need a repository that can execute “in situ” a broad range of algorithmic analytics?
For ELT pushdown, will we have processing that demands an RDBMS (say, for complex table joins)?
Are relational requirements minimal for the data lake?
Finding answers to some of these questions will let you conclude on which data lake platform is best for you to use. You need the most secure, scalable, comprehensive, and cost-effective portfolio of services that enable you to build your data lake in the cloud, analyze all your data, including data from IoT devices with a variety of analytical approaches including machine learning.
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