From databases to , the data landscape is changing rapidly as volumes and sources of data increase. With a growth projection of almost 30%, the market will grow from USD 3.74 billion in 2020 to USD 17.6 billion by 2026.and, finally, to
Also, from the 2022 Data and AI Summit, it is clear that is the future of and governance. The trend will likely grow due to the release of Delta 2.0 by , such that all of the platform's will be .
Plus, Snowflakes announced some game-changing features at its summit, making the mainstay of the industry. Governance, security, , and seamless analysis of analytical and transactional data, will be the prime factors driving innovation in this domain.
Table of Contents
Basic Anatomy of a
According to Hay, Geisler, and Quix (2016), the three main functions ofare to ingest from several , store it in a secure repository, and allow users to quickly analyze all data by directly querying the .
A three components. , file format, and table format. All these help with the functions mentioned above and serve as the primary building blocks of a ., therefore, consists of
Thefile format serves as the unit where are compressed in column-oriented formats to querying and exploration. Lastly, the table format helps with by aggregating all into a single table.
So updating onewill update all others as if they were all in a single table.
A Comprehensive List of 18Features
has become a necessity rather than a nice-to-have. But that doesn't mean an organization blindly invests in it. Different circumstances warrant the need for a different feature set. Below is a list of all the features a should ideally have.
Ability to Scale
Efficient is crucial for a to maintain so that a broader set of users can easily understand and derive insights from different .
Darmont and Sawadogo (2021) state that data within ahas no explicit format, which means it can quickly become a wasted asset without to describe the relevant .
The authors identify three levels ofthat a system should have. Firstly, it should provide business-level information to enhance understanding of a . Secondly, operational should cover information generated during , while technical should clearly describe the .
Carry out ACID Transactions
Awithout support for ACID properties can be a considerable setback for .
Wright et al. (2007) describe ACID as an acronym for Atomicity, Consistency, Isolation, and Durability.
Atomicity ensures that only completedaffect . So no row is added if an update fails midway.
Consistency maintainsby imposing constraints like unique identifiers, positive balances in a checking account, etc.
Isolation prevents concurrent operations from interacting, while durability helps maintain the latest data state even after a system failure.
Support for DML Operations
Database Manipulation Language (DML) is a set of commands that let users manipulate data in databases. For example, is a DML that allows users to write commands like SELECT, INSERT, DELETE, UPDATE and MERGE to perform specific operations on data.
having support for DML simplifies governance and along with change data capture (CDC) by letting users easily maintain consistency between source and target tables. For example, a user can deploy the UPDATE command to pass on changes detected in a source table to the target table based on specific filters.
Flexibility in Building and Maintaining
One of the advantages of a flexibility with . evolution require pre-defined before storing a particular , while do not need such a .over a is that provide
Effectivehave systems that automatically infer the from structured and that are in store. Such inference is usually termed -on-read instead of -on-write, where the latter term applies to the rigid structures of a .
Tracking row-level Table Changes
Delta Lake and Snowflake allow users to track and capture changes made to tables at the row level. The feature is part of CDC, where the records any change made to a source table due to an UPDATE, DELETE, or an INSERT event in a separate log.like
Such tracking helps in several, like optimizing procedures by only processing the changes, updating BI with only the new information rather than the entire table, and helping with by saving all the changes made in a change log.
MaintainingLog, Rollback & Time travel.
Managing is challenging if a lacks a versioning system. It especially becomes cumbersome if there is which means keeps coming in constantly. If some bad data enters the data stream, cleaning up such a large will be very difficult.
As such, automatic versioning, allowing time travel by letting users track and roll back to previous versions if needed and simplifying the management of to maintain and quality.must support
Data (Table) Restoration
It is common for businesses today to perform frequent migrations offrom one environment to another to use data solutions. But conducting such migrations on may lead to irreversible setbacks that can cause businesses to lose valuable .
So,should have built-in restoration capabilities that let users restore the previous state of the relevant tables using secured backups through simple commands.
Automated File Sizing
File sizes can quickly grow when dealing with large data clusters cannot adjust file sizes based on the . The result is that the system creates many files, with each file size being relatively small, thereby occupying a lot of unnecessary space.such as those found in applications. Traditional based on
Efficient Delta Lake, for instance, allows users to specify the file size of the target table or let the system adjust the size itself based on workload and the overall size of the table. Larger tables warrant larger file sizes so that the system creates fewer files.should automatically adjust file sizes based on the volume of incoming data.
Managed Cleaning Service
Nargesian et al. (2019) point out the lack of efficient data cleaning mechanisms in most as a glaring weakness that can quickly turn a into a . Since ingest data without a pre-defined , data discovery can become complex as the volume and increase.
As such, Snowflake impose certain constraints at the stage to ensure that the data coming in is not erroneous or inconsistent, which can later result in inaccurate analysis.platforms like
Indexing tables can enable to speed up query execution, using indices rather than traversing the whole to deliver results.
Indexing is especially useful when applying filters inqueries as it simplifies the search. can also play a role as it defines specific attributes of data tables for easy searchability.
However, a Snowflake does not use indexing since creating an index on vast . Instead, it can be time-consumingcomputes specific statistics on the columns and rows of the tables and uses those for query execution.like
store now and analyze later." That is the beauty of storing data in .feature in is sometimes not explicitly prioritized because work on the principle of "
However, this can quickly become a bottleneck and turn ainto a swamp with no use for . should therefore have some mechanism for providing early of the data to give users an idea of what it contains during the process.
Support for Bulk Loading
Although not a must-have, bulk loading is beneficial when data needs to be loaded into the occasionally in large volumes. Unlike loading data incrementally, bulk load helps speed up the process and improve performance.
However, higher speed may only sometimes be a good thing to have since bulk loading may ignore the usual constraints responsible for ensuring only clean data enters the lake.
Support for Concurrency
One of the problems with high concurrency, which meant serving several users together was a hassle. Cloud platforms addressed this problem, but high concurrency was still an issue, given the restrictions of .was that they could not provide
platforms like Apache , well-known for , cannot support high concurrency. However, solutions like Data Bricks are one of the few that support high concurrency, although they do not score very well on low latency, which is the time required to respond to user requests.
Support for Data Sharing
Data sharing has become the need of the hour with the ever-increasing pace of digitalization. With data being used for several by various teams, seamless data sharing through a system is necessary for decision-making and preventing between business domains.
It is not just thatshould provide ways to share data across platforms seamlessly, but they should do so safely and securely since data security can become an issue due to weak .
Abadi (2009) defines data partitioning as distributing data across multiple tables or sites to speed up query processing and simplify.
Khine and Wang (2018) write that sincedepend on , technologies and store both and , sensitive data may end up in the wrong hands within an organization.
should therefore allow for centralized control whose granularity can extend to even control access at the row level to ensure compliance with regulatory standards.
Ravat and Zhao (2019) define aas a solution that ingests data in various formats and serves different users, like , for like and while ensuring and security.
This definition makes it clear that one of the objectives of ais to help users perform and build systems that drive business competency.
Effectiveis crucial for to store valuable data (Derakhshannia et al., 2019). Indeed, organizations need to build a solution that provides an optimal ground between data access and data control.
Amust have processes to maintain and integrity as data sharing becomes the norm across several platforms. The processes become especially useful with with multiple users accessing different simultaneously.
Does Your IdealHave These Features?
It won't be a surprise if yoursolution does not have all the above features. A solution can only have some feature sets as it largely depends on the organization's needs that determine which ones are essential.
However, integrate.io provides several tools that cover most of the mentioned features and help processes for maintaining effective .
So talk to one of our experts to gain maximum value from your . initiative