Data warehouse (DWH) systems and data lakes are well-recognized storage repositories in the data industry. While data warehouses have had longer existence and recognition, the data industry has embraced the more recent repository, the data lake, especially after the growth of big data, shift towards cloud storage, and implementation of artificial intelligence (AI) technologies.
The most popular data lake tools are:
Infor Data Lake.
...and many more...
Depending on the business use case, data lakes and DWH can serve different purposes and offer various advantages.
One can argue that the advantages to data lakes include:
Faster access: data lakes can be readily accessible to users allowing them to achieve real-time analytics.
Adaptability: data lakes can store small-scale or gigantic volumes of data (even Petabytes).
Flexibility: data lakes can work with various data types and data sources.
Cost-effective: cloud data lakes are more affordable compared to on-premise data warehouses.
It is important to notice here that Data lakes follow an ELT approach which means that data is stored raw and it is afterwards when that data is structured, cleaned and analyzed. The risks associated with this approach is to end up with a Data swamp.
The appeal and novel capabilities of data lakes posed a huge threat to traditional data warehousing (DWH) systems. The main drawbacks to DWHs include high costs associated with rigid internal structures unadaptable to the evolving data environment and time-consuming regarding design and build out of complex data storages
Nonetheless, DWH solutions have adapted competitively by also offering cost-effective cloud storage options and making interfaces and features more agile and modern. Moreover, the need and demand for DWH is still high with benefits that include:
Efficiency: DWH data is structured and can be retrieved within milliseconds.
Trending analysis: because DWH is designed for query and analysis, it contains historical data that allows usersto answer a set of predefined questions over time.
Governance: since many DWH systems follow a methodology (such as Kimball or Inmon) based on internal data standards and policies, this helps data users agree on rules, standards, and interpretations.
Data lakes’ new paradigm suits AI needs when facing big data problems because the cleaning and structuring problem is handled by the data scientist who has to create specific scripts to adapt the data to its needs. However, many analytical or business users are better served with structured data because they do not perform deep exploratory analysis and usually, they already know what questions are trying to answer regularly (revenues, margins, stock, etc).
Therefore, hybrid solutions combining both structure and semi-structured data systems are increasing in popularity.
In order to better offer a unified data quality solution we have created Aqtiva which is the fisrt #BigDataQuality tool in the market.
We ourselves, have struggled to perform data-cleansing tasks and applying data governance to our projects in both Data lakes and warehouses. Aqtiva was born as a solution to these complexities: it can add a fast data quality validation to avoid data swamps in the data lake approach and to avoid long integration processes in the data warehouse approach.
Aqtiva helps data lakes by improving data quality and governance while at the same time speeds up processes in DWH systems.
Aqtiva’s goal is to make clean data accessible with the following features:
Ensures data entry accuracy and saves tedious data engineering hours wasted in data cleansing.
Allows users through its user friend platform to implement data quality rules with just a few clicks; no program coding needed.
Monitors data quality progress through Aqtiva’s reporting dashboard.
in Aqtiva we are commited to reverse the famous 80/20 data science dilemma where 80% of time spent cleaning and 20% of time spent analyzing.
Aqtiva is the preferred data quality solution for the modern data engineer.