Data Lake Query Best Practices and Industry Trends

Data lake query best practices and industry trends are crucial for unlocking the full potential of your data lake. A well-designed data lake query can help you extract valuable insights from your data.

Data lakes can store vast amounts of data, but querying them efficiently requires a structured approach. This includes defining a clear data catalog, implementing data governance, and using query languages like SQL and Spark SQL.

To ensure the scalability of your data lake, consider implementing a columnar storage system. This can significantly improve query performance, especially for large datasets.

Data lake solutions like Hadoop HDFS, Amazon S3, and Azure Blob Storage are used in the raw data store section to store ingested data in its native format.

These solutions provide a repository for data to be staged before any form of cleansing or transformation. They are often used in conjunction with other tools like Dremio or Presto for querying refined data.

The data lake solutions mentioned above are also used for storing raw data in the raw data store section, which is the first step in the data storage and processing layer.

Expand your knowledge: Data Lakehouse Companies

Sisense has built a data lake architecture using the AWS ecosystem to effectively manage and analyze its vast amount of product usage logs.

This data had accumulated to more than 70 billion records, making it a challenge to manage and analyze without a data lake architecture.

Sisense chose to use a data lake architecture to handle its massive data set, which is a common solution for companies with large amounts of data.

By using a data lake architecture, Sisense can now easily manage and analyze its data, making it a valuable asset for the company.

A data lake architecture allows for the storage of large amounts of raw data in its native format, which is ideal for Sisense's product usage logs.

This raw data can then be processed and analyzed as needed, providing valuable insights for the company.

Data lakes are particularly useful for companies with large amounts of unstructured data, such as product usage logs.

By using a data lake architecture, Sisense can now easily scale its data management and analysis capabilities as needed.

Sisense's data lake architecture is built on top of the AWS ecosystem, which provides a robust and scalable infrastructure for storing and processing large amounts of data.

This architecture allows Sisense to take advantage of the scalability and reliability of AWS, making it an ideal solution for the company's data management needs.

Sisense's data lake architecture is also highly secure, with built-in data encryption and granular access control policies to protect sensitive data.

This security feature is essential for companies like Sisense that handle large amounts of sensitive data.

By using a data lake architecture, Sisense can now easily manage and analyze its data, making it a valuable asset for the company.

Sisense's data lake architecture is a key component of its business intelligence software, enabling the company to make data-driven decisions.

This architecture allows Sisense to provide its customers with valuable insights and analytics, which is a key differentiator for the company.

Take a look at this: Data Lake Aws

Overall, Sisense's data lake architecture is a robust and scalable solution that meets the company's data management and analysis needs.

By using a data lake architecture, Sisense can now easily manage and analyze its data, making it a valuable asset for the company.

This architecture has enabled Sisense to provide its customers with valuable insights and analytics, which is a key differentiator for the company.

Sisense's data lake architecture is a great example of how a data lake can be used to manage and analyze large amounts of data.

By using a data lake architecture, Sisense can now easily scale its data management and analysis capabilities as needed.

This architecture has enabled Sisense to provide its customers with valuable insights and analytics, which is a key differentiator for the company.

Sisense's data lake architecture is built on top of the AWS ecosystem, which provides a robust and scalable infrastructure for storing and processing large amounts of data.

This architecture allows Sisense to take advantage of the scalability and reliability of AWS, making it an ideal solution for the company's data management needs.

For more insights, see: Aws Data Lake Formation

Sisense's data lake architecture is also highly secure, with built-in data encryption and granular access control policies to protect sensitive data.

This security feature is essential for companies like Sisense that handle large amounts of sensitive data.

By using a data lake architecture, Sisense can now easily manage and analyze its data, making it a valuable asset for the company.

Sisense's data lake architecture is a key component of its business intelligence software, enabling the company to make data-driven decisions.

This architecture allows Sisense to provide its customers with valuable insights and analytics, which is a key differentiator for the company.

Sisense's data lake architecture is a great example of how a data lake can be used to manage and analyze large amounts of data.

By using a data lake architecture, Sisense can now easily scale its data management and analysis capabilities as needed.

This architecture has enabled Sisense to provide its customers with valuable insights and analytics, which is a key differentiator for the company.

Suggestion: Data Lake Analytics Azure

An event-driven serverless architecture is a great way to work with unstructured data, and it's becoming increasingly popular. This approach allows you to query raw, unstructured data for real-time analytics, alerts, and machine learning.

Natural Intelligence adopted a data lake architecture based on AWS Kinesis Firehose, AWS Lambda, and a distributed SQL engine to effectively work with unstructured data. They used S3 as the data lake storage layer into which raw data is streamed via Kinesis.

To process the data, AWS Lambda functions were written in Python. The processed data is then queried via a distributed engine and finally visualized using Tableau. This setup enables real-time analytics and alerts.

Here are some key components of an event-driven serverless architecture:

By using a serverless architecture, you can scale your data processing capabilities up or down as needed, without having to worry about provisioning or managing infrastructure. This makes it a great option for handling large amounts of unstructured data.

AWS offers a robust data lake architecture anchored by its highly available and low-latency Amazon S3 storage service. S3 is particularly attractive for those looking to take advantage of AWS's expansive ecosystem.

Amazon S3 is integrated with various AWS services, including Amazon Aurora for relational databases, AWS Glue for robust data cataloging, and Amazon Athena for ad hoc querying capabilities. This well-integrated set of services streamlines data lake management but can be complex and may require specialized skills for effective navigation.

AWS Lake Formation architecture provides a user-friendly console for dataset search and browsing, simplifying data lake management for business users. The platform includes a suite of capabilities for data management, including data tagging, searching, sharing, transformation, analysis, and governance.

AWS provides a comprehensive yet complex set of tools and services for building and managing data lakes, making it a versatile choice for organizations with varying needs and expertise levels.

Here are some key features of AWS for data lakes:

* Amazon S3: highly available and low-latency storage serviceAWS Glue: robust data cataloging and metadata managementAmazon Athena: ad hoc querying capabilitiesAmazon Redshift: data warehousing solutionAWS Lake Formation: user-friendly console for dataset search and browsing

Broaden your view: Data Lake Engineering Services

Staying on top of cloud best practices and industry trends is crucial for a successful data lake solution. Get weekly insights from the technical experts at Upsolver to keep you ahead of the curve.

Cloud best practices are constantly evolving, and it's essential to adapt to the latest trends to ensure your data lake solution is scalable and secure.

Data lakes can grow exponentially, and proper management is key to maintaining performance and preventing data sprawl.

Suggestion: Data Lake Cloud

Data Lakes and Warehouses are compatible, but they don't maintain exact parity with each other. This means you'll need to understand the differences between the two storage solutions.

You can use Data Lakes as your only source of data and query all of your data directly from S3 or ADLS. This is a great option if you want to simplify your data management.

HeatWave is a feature that transparently connects to data lakes, letting users process and query hundreds of terabytes of data in the object store. It supports a variety of file formats, including CSV, Parquet, and Aurora/Redshift backups.

Autonomous Database Warehouse is another option that enables a self-service data lakehouse. This allows users to load or directly query files on all object stores, including OCI, AWS, Azure, and Google Cloud Platform.

When working with data lakes, it's essential to choose the right engines to extract data from your Azure Data Lake Storage (ADLS) destination.

Azure Databricks, Azure Synapse Analytics, and Dremio are all supported query engines for extracting data from ADLS.

To extract data using Azure Synapse Analytics, you have three options: Dedicated SQL pools, Apache Spark pool, or serverless SQL pool.

Azure Databricks is a popular choice for data engineers due to its ability to handle large-scale data processing.

Here's a summary of the supported engines for extracting data from ADLS:

When building a data lake, it's essential to consider the format in which you store your data. A data lake should store data in open file formats such as Apache Parquet.

You can also store data in other formats like Delta Lake and IcebergBeta, which are supported by Fivetran. These formats are ideal for Azure Data Lake Storage.

Data is stored in a structured format in the destination, making it easier to query and analyze. This is achieved by writing source data to Parquet files in the Fivetran pipeline.

A fresh viewpoint: Data Lake Store

The choice of table format depends on your specific needs and the tools you're using. For example, Databricks uses the table definition to understand the structure of the data.

Here are some common table formats used in data lakes:

Data lakes can also be optimized for use with systems like Power BI and Azure HDInsight or machine learning vendors like Azure Databricks or Azure Synapse Analytics.

Column statistics for Iceberg tables are updated based on the number of columns in the table.

If the table contains 200 or less columns, we update the statistics for all the columns. This means that every column in the table will have its statistics updated.

If the table contains more than 200 columns, we update the statistics only for the primary keys. This is a more targeted approach, focusing on the columns that are most critical to the table's structure.

Here's a summary of the column statistic update rules for Iceberg tables:

A data lake's schema is a crucial aspect of its functionality. It determines how the data is organized and structured, making it easier or harder to query and analyze.

Segment Data Lakes applies a standard schema to make raw data easier and faster to query. This schema is inferred from the data itself, with schema components such as data types being automatically detected.

A data lake's schema can be thought of as a map of the underlying data structure. This map is stored in a Glue Database, where it can be easily accessed and queried.

Data lakes support schema-on-read, which means the schema is not imposed upfront like in traditional data warehouses. Instead, the schema is inferred from the data as it's being read.

Segment Data Lakes partitions the data in ADLS by the Segment source, event type, then the day and hour an event was received by Segment. This ensures that the data is actionable and accessible.

If this caught your attention, see: Data Lake Schema

Data types supported in Segment Data Lakes include:

As data is ingested into a data lake, the schema may evolve to accommodate new data types or formats. This can happen automatically, with Data Lakes attempting to cast incoming data into the existing schema.

However, if the data type in Glue is wider than the data type for a column in an ongoing sync, the column may be dropped if it cannot be cast. This highlights the importance of monitoring and managing schema evolution in a data lake.

Data lakes can handle semi-structured or unstructured data, making them ideal for modern data types like weblogs, clickstreams, and social media activity. This flexibility comes at the cost of requiring more extensive management to ensure data quality and security.

Readers also liked: Delta Lake Data Types

A data lake is an architecture pattern that stores large amounts of unstructured data in an object store, such as Amazon S3, without structuring the data in advance.

This approach is ideal for businesses that need to analyze data that is constantly changing or very large datasets. Data lake architecture is the combination of tools used to build and operationalize this type of approach to data, including event processing tools, ingestion and transformation pipelines, and analytics and query tools.

The Depop team adopted a data lake approach using Amazon S3 after realizing that performance tuning and schema maintenance on Redshift would be cumbersome and resource intensive. Their data lake consists of three different pipelines: ingest, fanout, and transform.

Here are some key design principles for building a data lake:

A data lake is an architecture pattern, not a specific platform, built around a big data repository that uses a schema–on–read approach. This means we store large amounts of unstructured data in an object store like Amazon S3 without structuring it in advance.

Data lake architecture is a combination of tools used to build and operationalize this approach to data. These tools include event processing tools, ingestion and transformation pipelines, and analytics and query tools.

Businesses that need to analyze data that's constantly changing or very large datasets find data lakes ideal. This is because data lakes maintain flexibility to perform further ETL and ELT on the data in the future.

A data lake is built around a big data repository, and it's essential to understand that once a data type is set for a column, all subsequent data will attempt to be cast into that data type. If incoming data doesn't match the data type, the system tries to cast the column to the target data type.

Event sourcing is a crucial design principle when building a data lake, as it allows you to store all incoming events in an immutable log, which can then be used for ETL jobs and analytics use cases.

Storing data in open file formats is also essential, as it enables ubiquitous access to the data and reduces operational costs. Apache Parquet is a popular choice for this purpose.

Optimizing for performance is vital, as it allows you to store data in a way that makes it easy to query. This can be achieved by using columnar file formats and keeping files to a manageable size.

Data governance and access control are also critical, as they enable you to control access to data in a data lake and address security concerns. Tools like AWS data lake formation can make this process easier.

Schema visibility is another key principle, as it allows you to understand the data as it is being ingested in terms of the schema of each data source, sparsely populated fields, and metadata properties.

Here are the 5 key design principles and best practices for building a data lake:

A data warehouse is a traditional system designed to support analytics, allowing organizations to query their data for insights, trends, and decision-making.

Data warehouses require a schema upfront, which means they're less flexible, but they can handle thousands of daily queries for tasks like reporting and forecasting business conditions.

The ETL (Extract, Transform, Load) process usually occurs before data is loaded into the warehouse, but some organizations deploy data marts, which are dedicated storage repositories for specific business lines or workgroups.

Cloud data warehouses like Snowflake, BigQuery, and Redshift offer advanced features, making them a significant improvement over traditional data warehouses.

Data warehouses are ideal for tasks like reporting and forecasting, but they may not be the best choice for advanced analytics activities, including real-time analytics and machine learning.

Here are some key differences between data warehouses and data lakes:

Data warehouses can be integrated with data lakes, allowing for a more comprehensive view of an organization's data.

Snowflake is a game-changer in the data lake landscape, redefining what's possible with its cross-cloud platform.

Its speed and reliability are unparalleled, thanks to an elastic processing engine that eliminates concurrency issues and resource contention. This makes it a top vendor in the field.

Snowflake breaks down data silos and enables seamless integration of structured, semi-structured, and unstructured data. This is a major advantage over traditional data lakes.

Its focus on flexibility and simplicity is key to its success, often described by data professionals as a platform that "just works." This means users can focus on their work without worrying about technical issues.

Snowpark and Snowpipe are advanced features that facilitate multilanguage programming and data streaming. These features make it easy to work with different types of data.

Automatic micro-partitioning, rest and transit encryption, and compatibility with existing cloud object storage are just a few of its efficient storage capabilities.

For your interest: Create Azure Data Lake Storage Gen2

Type transformation is a crucial step in data lake architecture, ensuring that data from various sources is accurately mapped to the destination data types. This process helps maintain data consistency and integrity.

For instance, a BOOLEAN value from Fivetran will be transformed to a BOOLEAN value in both Delta Lake Table Format and Iceberg Table Format. This means that boolean values remain unchanged during the transformation process.

The type transformation process also involves mapping data types from Fivetran to corresponding data types in Delta Lake and Iceberg formats. Here's a summary of the type mappings:

These type mappings are essential for ensuring that data is accurately transformed and stored in the destination data lake.