What is Data Lake: Storage and Data Processing- Part 1
For your business to have the best data lake practices, BI tools are the go-to solution with data analysis for customer experience metrics. But businesses now are going beyond BI to meet with the latest data lake essentials.
That helps to stream better, to interact, analyze, and more to get advantages of data lake at its best. Now, a question arises to you for how BI tools analyze small sets of relational data?
Tools help to get sets of data in a data warehouse that requires small data scans to execute further.
As per the latest market search: ”The data lakes market worldwide is expected to grow at a CAGR of around 28% during the period 2017-2023.”
For numerous data series, Sigma Data System will take you through the architecture of a Data Lake that explores across two dimensions:
- Part I – Storage and Data Processing
- Data Processing ETL/ELT
- Part II – File Formats, Compression, and Security
- File Formats and Data Compression
- Design Security
What is Data Lake?
In the world full of data, you need a storage that holds all your business data with security. A data lake is one such storage repository that is best for a business to hold a vast amount of original data until it is used.
Here comes the comparison for Data Lake vs. Data Warehouse to store large volumes of data. Data warehouse stores data in a hierarchy format or as a folder. It stores data that undergoes a predefined process for a specific use.
Whereas Data Lake uses a simple data storage process in the form of enterprise data lake architecture that is linked with Hadoop object storage. So, once the source data is in a central lake without any solo control over a schema embedded, at a time sustaining an additional use case is a more simple implementation.
Let’s look at best practices in setting up and managing data lakes across three dimensions –
- Data ingestion
- Data layout
- Data governance
To build organizational data more reliable and structured that can be accessible by end-users irrelevant to an industry like data engineers, analysts, data scientists, product managers and more. Data Lake is beneficial to assist better business insights in a cost-effective way to enhance overall business performance.
The main benefit of having a data lake is to get the advanced data analytics services that are possible only through data lakes.
In order to create a data lake, we should take care of the data accuracy between source and target schema.
For instance, record counts match between source and destination systems. More towards key considerations, the following principles are needed for cloud-based data lake storage.
1. High durability
Without resorting to the high-availability of data and designs as the main repository of serious business data, very high stability of the core storage coat allows for excellent data strength.
2. High scalability
Any huge volume of enterprise-level data needs to store with proper security and Data Lake is best proposed to stockpile massive data centrally. The Scalability of the enterprise data is a must as a whole when it comes to data scaling without running into fixed arbitrary capability limits.
3. Unstructured, semi-structured and structured data
Original data can be in any format. So to store all types of data within a the main design structure is mandatory and is possible with Data Lake in a particular storage area. JSON, XML, Text, Binary, CSV, are some of the examples of data storage.
4. Independence from a fixed schema
As we know, schema development is a basic need for the data industry where the ability to implement schema matters a lot. Schema development requires reading data as required for every use, can only be proficient if the underlying core storage layer does not dictate a fixed schema.
For Data Lake, it is advisable to permit your system with growing data for a quick scaling. Open source has zero payment cost and will be in charge of data models and cold/hot/warm data along with suitable compression techniques to avoid the increased cost.
6. Separation from compute resources
The most significant philosophical and practical advantage of cloud-based data lakes as compared to “legacy” big data storage on Hadoop/HDFS is the ability to decouple storage from compute and enable independent scaling of each.
7. Complimentary to existing data warehouses
A data warehouse is a storage pull for filtered and data in a structured format that is used for a specific purpose. So for a native base huge business data, Data Lake is definitely a complementary work for integrated data.
Speed up your Data Lake operations with Sigma Data Systems –
- Multi-cloud offering – A multi-cloud offering helps to keep away from cloud vendor lock-in by contributing a native multi-cloud platform along with support for their corresponding native storage. Options for the native storage are Azure Data Lake and Blob, Google Cloud Storage, AWS S3 Object Store.
- Unified data environment – What if the integrated data environment is not been allocated? An integrated data environment is mandatory as it helps to get connectivity to legacy Data Warehouses and NoSQL databases in the cloud.
- Intelligent and automatic response – For storage and computed data, both are in need of random big data work. As it estimates the current workload to automatically predict the additional work and make an intelligent reason on time.
- Support for various mechanisms – Data Lake helps to accelerate Encrypted data at the break in an organization with your selected cloud vendor.
- Multiple distributed big data engines – Spark, Presto, Hive, and other common frameworks are multiple engines that allow data teams to solve a wide variety of big data challenges.
- Support for Python/Java SDKs – It allows easy business data integration to your applications for structured data and to use it for better functioning.
- Ingestion and processing from real-time streaming data sources –
Integration with well-admired ETL platforms helps data teams to address the real-time use cases through Talend, and Informatica platforms that increase speed adoption by traditional data teams.
- Multiple facilities for data Import/Export – With the help of different embedded tools, big data teams can import the data and run analyses to export the output of your preferred data visualization services.
The data storage practices help to get all data sorted well with Data Lake that builds numerous advantages using the collected business data. Cloud offers regularly growing the range of services they offer and big data processing seems to be in the center with AWS data lake solution architecture.
A cloud data lake can break down data silos and assists several analytics workloads at lower costs.