Most of the data is kept in a “data lake,” a centralized and unprocessed area. A data lake uses a flat design and object storage to store the data instead of a hierarchical data warehouse, which arranges the data into files and folders. Data is stored in object storage with metadata tags and a unique identifier, which enhances performance and makes it simpler to find and retrieve data across regions. Data lakes make it possible for numerous apps to use the data by utilizing open standards and cheap object storage.
Although data warehouses offer businesses very effective and scalable analytics, they are costly, proprietary, and unable to handle the current use cases that most companies are looking to address. As a result of data warehouses’ shortcomings, data lakes were created. Data lakes enable storing all of an organization’s data in a single, central location where it can be preserved “as is,” in contrast to a data warehouse which imposes a schema up front, which is a structured arrangement of the data.
All data types, including unstructured and semi-structured data like images, audio, video, and documents, may be processed by data lakes, which is crucial for today’s machine learning and advanced analytics use cases. A data lake can hold data at all phases of the refinement process, including intermediate data tables created during the refinement of raw data. Along with an organization’s structured, tabular data sources, unstructured data can be ingested and stored (such as database tables). As opposed to the majority of databases and data warehouses, this.
The purpose of using a data lake
A data lake is an obvious choice for data storage due to its unique capacity to absorb raw data in several formats (structured, unstructured, and semi-structured), as well as the other advantages mentioned. Customers are less likely to be forced to adopt a proprietary solution like a data warehouse because data lakes are open-format, which is more crucial in modern data infrastructures. Because they can grow and use object storage, data lakes are also very affordable and durable. Additionally, sophisticated analytics and machine learning on unstructured data is among businesses’ top strategic concerns.
Top Data Lakes Tools/Solutions
Developers, data scientists, and analysts may store data of any size, shape, or speed and do any kind of processing or analytics across platforms and languages, thanks to the wealth of features that Azure Data Lake offers. Azure Data Lake streamlines batch, streaming, and interactive analytics by eliminating the challenges of ingesting and storing all data.
Key characteristics of Azure Data Lake
- Using autonomous geo-replication offers infinite scale and data longevity.
- capable of completing challenging tasks with the same level of performance
- very secure with adaptable protection methods for data access, encryption, and network-level control
- Cost reduction via separate storage and computing scalability
- A single storage platform that supports the most popular analytics frameworks and allows for ingestion, processing, and visualization
An open-format storage layer called Delta Lake offers dependability, security, and performance for both batch and streaming operations. Delta Lake provides a single storage area for organized, semi-structured, and unstructured data and is affordable and highly scalable.
Key characteristics of Delta Lake
- A single authoritative source for all data, including real-time streams and reliable, high-quality data
- transparent and safe data sharing
- Excellent performance with Apache Spark as the engine
- Free and flexible
- Data engineering that is automated and reliable
- Scaled-up security and governance
Snowflake’s cloud-based data warehouse firm offers a fully managed solution with great concurrent workload scalability. The cross-cloud platform can access controlled data self-service for various workloads without facing resource or concurrency concerns. It provides an Amazon Web Services-based cloud data repository.
Key characteristics of Snowflake
- Structured, semi-structured, and unstructured data of any kind can all be combined on a single platform.
- Swift, dependable querying and processing
- Dependable cooperation
Qubole is essentially an open data lake startup that improves the capabilities of data lakes for machine learning and other analytical processing.
What is an open data lake, you may wonder? Simply put, this specific data lake has data in an available format accessible through open standards.
Key characteristics of Qubole
- Because of its interaction with Presto, Tableau, and Looker, it offers ad-hoc analytics reports. It would only take one click to complete the process.
- One cohesive insight can be obtained from combining several streaming data pipelines, and that too in real-time.
- Efficient data pipeline management to avoid bottlenecks and maintain SLAs.
The Infor Data Lake solution gathers data from many sources and ingests it into a structure that immediately begins to extract value from it.
Key characteristics of Infor Data Lake:
- Infinite scrolling of the store would still allow for the most intelligent choices to be made using the most enriched data that can be included in ML algorithms.
- It won’t ever become a swamp where your data is stored. Your data should be intelligently cataloged to guarantee that understanding is never lost.
- The relational layer created by Infor’s Data Lake Metagraph creates extensive connections between multiple data types and datasets. In the latter stages, this can be used to draw a wise conclusion.
With the help of Informatica’s Intelligent Data Lake, users can get the most out of their Hadoop-based data lake.
Other data solutions are supported, including Microsoft Azure SQL Database, AWS Redshift, Amazon’s Aurora, and SQL Data Warehouse.
Key characteristics of Intelligent Data Lake
- Using large-scale data searches won’t require much coding because of the underlying Hadoop framework.
- Detail relations between various data sets can be constructed using a graph-based processing engine to provide more clarity about the entities that are essential to your organization.
- Informatica Enterprise Informatica Catalog will have no trouble generating customized scanners to read the sources, regardless of whether the databases are older or were explicitly designed for a start.
A big data processing platform built in the cloud called Cloudera Data Lake Service aids in businesses’ efficient management, processing, and analysis of enormous amounts of data. ETL, data warehousing, machine learning, and streaming analytics are just a few of the workloads for which the platform is well suited because of its ability to manage both organized and unstructured data.
Additionally, Cloudera offers the Cloudera Data Platform (CDP), a managed service that makes it simple to install and maintain data lakes in the cloud. Because it provides a wide range of features and services, it is one of the best cloud data lake options.
Key characteristics of Cloudera data lake service
- Petabytes of data and thousands of different users can be handled using CDP.
- Cloudera governance and data log features transform metadata into information assets, which also increase its usefulness, dependability, and value over the course of its life cycle.
- Users can control encryption keys, and data can be encrypted at rest and while in motion.
- In addition to defining and enforcing configurable, role- and attribute-based security rules, Cloudera Data Lake Service also prevents and audits illegal access to sensitive or restricted data.
- End users can access the platform with just one sign-on (SSO) through the secure access gateway of Apache Knox.
A cloud-based storage system called Google BigLake integrates data lakes and warehouses. Users can store and analyze data of any amount, kind, or format using this tool. The platform is scalable and straightforward to combine with other Google goods and services. To help assure data quality and compliance, BigLake also includes several security and governance measures.
Key characteristics of Google BigLake
- The critical open data formats supported by BigLake, including Parquet, Avro, ORC, CSV, and JSON, are built on open standards.
- Users can access BigLake tables and those made in other clouds like Amazon S3 and Azure Data Lake Gen 2 in the data catalog because it supports multi-cloud governance.
- Users can maintain a single copy of their data and make it accessible across Google Cloud and open-source engines like BigQuery, Vertex AI, Spark, Presto, Trino, and Hive via BigLake connectors.
The open-source framework Apache Hadoop stores and handles large amounts of data. It is made to offer a dependable and scalable environment for applications that must swiftly process enormous payments of data. Some leading companies that provide Hadoop-based software include IBM, Cloudera, and Hortonworks.
Key characteristics of Hadoop
- The Hadoop data lake architecture comprises several components: YARN, MapReduce, HDFS (Hadoop distributed file system), and Hadoop common.
- Hadoop holds a variety of data kinds, including log files, pictures, web pages, and JSON objects.
- Data processing may be done concurrently, thanks to Hadoop. This is because data is divided and spread across different cluster nodes as it is consumed.
- Users can compile information from various sources and serve as a relay point for data overburdening another system.
The abbreviation “S3” in “Amazon S3” stands for Simple Storage Service. It is an object-based storage service from a technical perspective, where you may store highly unstructured material like pictures, movies, and audio.
Because all data is kept in a single bucket, the object-based storage service makes it simple to store or retrieve the data (in a flat directory). For users’ convenience, Amazon provides directories organized into folders. However, the objects are actually kept in a folderName/fileName.fileExtension format.
Key characteristics of Amazon S3
- Because they are less likely to alter over time, Amazon S3 data lakes are ideal for storing unstructured data.
- Amazon offers users built-in machine learning integration choices, mainly its own tool called Amazon SageMaker, to handle and analyze highly unstructured data stored in an S3 data lake.
- Users can create, train, and employ machine learning (ML) models and gain insights from the massive amount of unstructured data.
- S3 provides uniform data access, security, and governance, which makes it easier to immediately adhere to the critical sector- and/or location-specific regulatory requirements.
- Organizations may quickly start using secure S3 data lakes thanks to the AWS lake formation. Amazon S3 offers multiple pricing options and is readily scaleable for data lakes.
Note: We tried our best to feature the best data lakes available, but if we missed anything, then please feel free to reach out at Asif@marktechpost.com
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Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications