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Benefits of Snowflake Time Travel

• • Querying data that has been modified or erased in the past
• • Duplicating entire tables, schemas, or databases at or before specific dates
• • Restoring deleted tables, schemas, and databases

How to activate Snowflake Time Travel?

Activating Snowflake Time Travel is a simple process that requires no additional effort. It is automatically activated with a retention period of one day. Nonetheless, upgrading to the Snowflake Enterprise Edition is necessary to customise the Data Retention Period and extend it to 90 days for Databases, Schemas, and Tables. It's important to note that increasing the Data Retention Period results in additional storage usage, reflected in your monthly Storage Fees.

Data Retention Period in Snowflake

In Snowflake, Data Retention Period determine how long historical data is retained to support Time Travel functionality. When data in a table is altered, such as through deletions or updates, Snowflake maintains the previous state of the data so that Time Travel operations (like SELECT, CREATE...CLONE, UNDROP) can be performed on it. By default, all Snowflake accounts have a standard retention period of one day (24 hours).

However, the Retention Period can be adjusted at the account and object level in the Snowflake Standard Edition to 0 (or unset to the default of 1 day) for databases, schemas, and tables.

In the Snowflake Enterprise Edition or higher, the Retention Period can be set to 0 for temporary databases, schemas, tables, and temporary tables. For permanent databases, schemas, and tables, the Retention Time can be configured to any duration between 0 and 90 days.

Functions of Snowflake Time Travel SQL Extensions

Snowflake Time Travel SQL Extensions are special SQL commands that allow users to query historical data from a specific point in time using the Time Travel feature. These extensions enable users to perform various Time Travel operations, including:

• a. CLONE: This command creates a copy of a table, schema, or database at a specific point in time using Time Travel.
• b. UNDROP: This command restores a dropped table, schema, or database to a specific point in time using Time Travel.
• c. HISTORY: This command retrieves the history of changes made to a table, schema, or database over time using Time Travel.
• d. AS OF: This command retrieves data from a table as it appeared at a specific point in time using Time Travel.

Specifying a Custom Data Retention Period for Snowflake Time Travel

To specify a custom Data Retention Period for Snowflake Time Travel, you can use the DATA_RETENTION_TIME IN_DAYS argument in the command when creating a table, schema, or database. By default, the maximum Retention Time in Standard Edition is set to 1 day (i.e. 24 hours), while in Snowflake Enterprise Edition (and higher), it can be set to any value up to 90 days.

The Data Retention Time can be set in the way it has been placed in the example below.

To create a schema with a custom Data Retention Period of 60 days, you can use the following SQL command:

create table mytable(col1 number, col2 date) data_retention_time_in_days=60;

Modify the Data Retention Period for Snowflake Objects

To modify the Data Retention Period of a Snowflake object, any change made to the Retention Period affects both active data and data in Time Travel. Depending on whether the period is increased or decreased, the following impacts occur:

• a. Increasing Retention
• b. Decreasing Retention

Let’s dive deep into more details:

a. Increasing Retention
Snowflake Time Travel preserves the data for a more extended period. For instance, if a Table’s Retention Time is increased from 10 to 20 days, the data set to be deleted after ten days will be retained for an additional ten days before being moved to Fail-Safe. However, data over ten days old and already transferred to Fail-Safe mode is unaffected.

b. Decreasing Retention
The duration of data stored in Time Travel is reduced. The shorter Retention Period applies only to active data updated after the Retention Period is shortened. If the data is still within the new Retention Period, it stays in Time Travel; otherwise, it is placed in Fail-Safe Mode. For instance, if a table with a 10-day Retention Period is reduced to 1 day, data from day 2 through day ten will be transferred to Fail-Safe, and only data from day one will be accessible through Time Travel.

Since the background process moves the data from Snowflake Time Travel to Fail-Safe, it may take some time to see the changes. Although Snowflake guarantees that the data will be transferred, it does not specify when the process will be finished. The data remains accessible via Time Travel until the background process is completed. To change an object's Retention Period, use ALTER object command, such as the following command for modifying a table's Retention Period:

alter table mytable set data_retention_time_in_days=30;

Snowflake Time Travel Data Query

To query previous versions of data in Snowflake Time Travel, you can use the AT | BEFORE Clause after making any DML actions on a table. This clause allows you to query data at or before a certain point in the table's history throughout the retention period. The specified threshold can be either time-based (e.g., a timestamp or time offset from the present) or a statement ID (e.g., SELECT or INSERT).

For example, to select historical data from a table as of a specific date and time, you can use a query like:

sql
SELECT * FROM my table AT (TIMESTAMP => 'Fri, 05 May 2023 16:20:00 -

If you want to pull data from a table that was last updated a certain number of minutes ago, you can use a query like:

sql
SELECT * FROM my_table AT(OFFSET => -60*5);

And to collect historical data from a table up to a specified statement's modifications, but not including them, you can use a query like:

Sql
SELECT * FROM my_table BEFORE(STATEMENT => '8e5d0ca9-005e-44e6-b858-a8f5b37c57

How to Restore Deleted Objects by Utilising the UNDROP Command?

To restore a deleted object that hasn't been permanently removed from the system (meaning it can still be seen in the "SHOW object type> HISTORY" output), you can use the UNDROP command in conjunction with Snowflake Time Travel. This command can be applied to various objects, such as tables, schemas, and databases. It effectively reverts the thing to its previous state before it was deleted with the DROP command. For example, the UNDROP command can also restore a dropped database.

Summing Up

Snowflake Time Travel’s features can enhance your decision-making process and overall data experience. If you're looking for a Snowflake service provider, Beinex is an excellent option. Our partnership with Snowflake enables us to offer advanced features like automated tuning, elastic compute, and analytics modernisation services to help your organisation realise exponential Returns on Investment.

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Challenges in Data Governance

Organizations often face challenges aligning with business goals to ensure data quality, security, and visibility.  Alation's Data Catalog centralized data management and enhances accessibility, helping businesses address the data governance challenges by managing data in line with the policies and standards.  Some of the challenges in data governance are as follows: • Issues in Data Quality: This happens due to incorrect or insufficient data in the system, which can result in expensive errors and affect decision-making.  Enterprises must follow continuous monitoring to ensure high data quality and maintain trust in data assets. • Struggling with Data Silos: For effective data governance, organizations must break down data silos as the separate storing of data across departments could hinder data accessibility and sharing, resulting in inefficiencies. • Concerns about Compliance and Security: To avoid sensitive data breaches, organizations must comply with the regulations and standards and enforce strong security measures.  Ignoring the compliance requirements can result in reputational damage, legal consequences, and hefty penalties.

More About Data Catalog

A Data Catalog is a warehouse of data assets that improves comprehension, governance, discovery, use, and management of data. It helps unify extensive and intricate data ecosystems into a single hub and breaks down silos, leveraging data the right way. The centralized view of enterprise data assets provided by the data catalog allows leaders to effectively drive cross-collaboration and scale data usage. Despite being a data repository, a modern data catalog assists in making business processes more data-driven. From enhancing operational efficiency to boosting customer experience to making strategic decisions, a data catalog is equipped to make the most of the data. A data catalog facilitates business decisions by letting people locate, understand, and trust the required data. Some of the fundamental functionalities and features of a data catalog are as follows: • Managing metadata: Brings together metadata from diverse sources into a centralized platform and offers a comprehensive picture of data across your enterprise. • Automating data discovery and search: Employs advanced search capabilities (search by tags, keywords domains, natural language, etc.), AI, and ML to locate and access relevant data assets. • Ensuring data quality: Allows data customers to understand data quality and build trust in the data through documentation of quality regulations, displaying data quality metrics, and quality profiling. • Tracking data lineage: Tracks the data flow from its source to destination, mapping the critical data aspects throughout the organization during the transformation. It also includes metadata about the transformation and data assets, enabling impact analysis. • Fortifying data governance: Enables data classification to assign suitable policies for ensuring compliance with regulations.

How Alation's Data Catalog Strengthens Modern Data Governance

Companies with data catalogs are more likely to acquire and retain customers and achieve profitability than those that do not have one. The following aspects elaborate on how Alation unlocks smarter data governance with its data catalog. • Breaking down data silos and centralizing data access: The Alation Data Catalog helps businesses struggling with data silos by centralizing data access and enabling easy data discovery and retrieval from a unified platform. Centralizing facilitates collaboration between departments by eliminating barriers between them. The enhanced collaboration enables effortless sharing of data assets and insights, fostering better decision-making and collaboration. • Managing metadata: Metadata management is paramount to data governance. With Alation Data Catalog, users can access powerful metadata management capabilities to handle data regulations, relationships, and definitions effectively. It allows users to understand and gain trust and confidence in their data assets. With features like end-to-end data lineage, automated metadata harvesting, and policy enforcement, Alation ensures data accuracy, accessibility, and compliance. • Enhancing data quality through Data Profiling and Cleansing: Data quality stays crucial for any organization to ensure trustworthy analytics and reporting. The Alation Data Catalog's data profiling and cleansing tools help detect inconsistencies and inaccuracies in data, helping enterprises maintain high data quality standards. • Guaranteeing compliance and security: With the Alation Data Catalog, compliance, and security can be ensured by implementing access controls and permissions. It entails protecting sensitive and confidential information by enabling the restriction of data access based on roles.   • Fortifying data security: The comprehensive audit trails and monitoring offered by the Alation data catalog are important for data security as they facilitate tracking data usage and changes over time. It also helps identify possible breaches and unauthorized access, enhancing accountability and transparency across the enterprise. • Making progress through continuous monitoring: Conducting routine audits to evaluate compliance and data quality is vital for ensuring data governance remains effective and adaptive to the dynamic requirements. Alation Data Catalog's monitoring tools offer insights into the use of data and the likelihood of serious security breaches, enabling informed decisions about policy modifications. It is important for businesses to invest in training programs for data users as they help them understand the functions of data catalog and apply the best practices. With the Alation Data Catalog, businesses can promote collaboration and maintain data integrity and safety. Alation's holistic approach to data governance builds a trustworthy and accountable culture. The Alation Data Catalog functions as a powerful enabler, equipping enterprises to thrive in a data-driven world by streamlining complex governance tasks and promoting a culture of data literacy. In partnership with Alation, Beinex equips businesses with the support to fulfill data governance requirements while streamlining implementation and saving time. Connect with us for a demo: Beinex - Beinex: Your Trusted Alation Partner in Dubai, UAE, MEA, KSA & UK for Data Intelligence

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. A farmer must be knowledgeable about soil, climate, and market. If something goes wrong with any of these areas the result will be devastating. This is where one of mankind’s biggest inventions, the Internet, offers the most reliable assistance. The Internet of Things has a plethora of devices that can make the work of a farmer far easier and more productive.

Smart Sensors

Smart sensors are the number one in a huge list of IoT devices being used in agriculture. These devices can be used to gather data about various aspects of agriculture like the humidity, acidity or mineral contents of the soil. Earlier these kinds of knowledge were something that farmers were supposed to gain after many years of experiences of both gain and loss. Or they had to depend on time taking and distant laboratories. But even a lab test had a limited chance of being hundred percent accurate as the soil’s quality could change in the blink of an eye. Smart sensors can provide the farmer with accurate data in real time. The data collected can precisely calculate and predict many changing or evolving aspects like the humidity content of the soil. These data can help decide:

1. The crops to be planted
2. The manures to be used
3. The amount and timing of watering
4. The market price for the final product

Drones

Next in the line is the use of drones. These can be used to:

1. Observe vast areas and collect important information that can help decide the most efficient methods of cultivation.
2. Provide the farmer with a bird’s eye view, thereby reducing the time and effort wasted in surveying the land personally.
3. Assist in remote application of pesticides and herbicides.
4. Track the cattle or scrutinize their health.

The data collected by sensors and drones can be used to create a plan through which the farmers can guarantee a good output with minimum input and loss. This will reduce a great percentage of both financial and manual investment. Besides, instead of depending on one’s gut feelings, a farmer can take a multitude of important decisions with scientifically backed technology. IoT devices can be easily operated by the farmers by connecting it to a laptop or mobile phone. There are various apps that are already very popular for providing agricultural data. There are also various government backed projects aimed at collection of data, providing online solutions and even arranging financial support for the smart framer.

Smart Greenhouses

Another IoT contribution is Smart Greenhouses. Greenhouses always contain plants which are out of their natural habitat and therefore need extra care. IoT enabled greenhouses can help:

1. Monitor the procedures of watering
2. Adjust the humidity inside
3. Analyse and provide proper lighting
4. Ensure proper balance in delicate matters like the level of carbon dioxide, temperature, etc.
5. In disease control by providing a close watch on all the plants’ vitals.

Such precise and high level of attention makes sure that there is no contamination in the greenhouse environment that could lead to spread of diseases. It can also help in detection or prevention of theft, which is a big risk especially if the plants are of rare or protected species.

Livestock Management

IoT in livestock management is bringing about revolutionary changes. With the spread of COVID-19, it is now a very crucial requirement that the livestock are healthy throughout their life. Even a small disease in the animal could turn out to be dangerous for the human who finally consumes it. Also spread of diseases in small animals like poultry can lead to devastatingly large loss for the farmer. Smart devices can help:

1. Track the vitals of each animal or bird through devices which are wearable.
2. Alert the farmer of even slight variations in the vitals via SMS, notifications, etc.
3. Administer medicines at the correct time, to any number of animals without having to keep manual tabs

All these crucial routines help in keeping diseases in check and thus reduce livestock loss.

There are many more IoT powered devices that are revolutionizing the field of livestock management like Geo-tagging and Geo-fencing.

1. Geo-tagging: It is used to locate the cattle that has strayed away from the herd or gone missing. Geo-tags are especially useful in case of natural calamities like flood, tsunami, landslide, etc. For example, the farmers of Kerala in South India used geo-tags to locate their animals that had either gone missing or died in the flood of 2018.
2. Geo-fencing: It is another popular method and it uses GPS to monitor and keep the cattle within a boundary without actual fencing. This can help in detecting cattle theft or even wild animal attacks on the livestock.

The Limitations and Solutions

IoT is already here to stay in the fields of agriculture, and there is more to come. But like anything else IoT in agriculture has certain cons. These limitations can thankfully be eliminated.

The Future

The agriculture IoT market is estimated to grow from USD 11.4 billion in 2021 to USD 18.1 billion by 2026 at a CAGR of 9.8% during 2021-2026, as per ResearchAndMarkets.com.

A few insights can be:

1. Precision Aquaculture: The agriculture IoT market for the precision aquaculture application segment is projected to register the higher CAGR during the forecast period, by application. Increasing demand for real-time tracking of fishing activity is the major reason behind the high growth of the agriculture IoT market in aquaculture farm monitoring applications.
2. Production Planning: The production planning stage segment of the agriculture IoT market is estimated to register the largest market share in 2026, by the farm production planning stage.
3. Small Farm Segment: The agriculture IoT market for small farm segment is projected to register the higher CAGR during the forecast period, by farm size. Small farms are expected to adopt automation and other advanced technologies at the highest rate in the coming years due to the reducing cost of farm automation equipment and advancements in technology that make it more feasible to deploy automation tools even on smaller farms to achieve high returns on investments.
4. Automation & Control Systems: The automation and control systems for the precision farming hardware segment of the agriculture IoT market is estimated to register the largest market share in 2026, by hardware type. The increasing demand for drones/unmanned aerial vehicles (UAVs) is a major reason behind the high growth of the market for automation and control systems.

APAC is likely to be the fastest-growing agriculture IoT market during the forecast period. Agriculture IoT techniques are expected to be adopted at a high rate in the region. This region consists of emerging countries such as India, China, and countries in Southeast Asia. Rapidly growing population, availability of arable land, and strong government support for farmers through subsidies in these regions are the major factors driving the adoption of agriculture IoT technologies in APAC.

These projections show that IoT based agriculture is here to make a big splash and is a boon of science.